SyncVals · Trajectory

simjeb-bracket-fea-mass-prediction-real

claude-code claude-opus-4-8 ✓ resolved GOOD_SUCCESS ↑ View task

Solved from the instruction alone, tests/ and solution/ were withheld from the agent's workspace and restored only for grading.

Reward = tests/test.sh exit code (0 → resolved); the classification below is post-hoc and cannot change it.

Classification , post-hoc; cannot change the reward

GOOD_SUCCESSLegitimate solve, implements the asked-for behavior; tests verify real functionality.

SubtypeLegitimate Solution via Rigorous Methodology

EvidenceThe agent achieved a verified score of 0.57 (reward 1.0, passing the 0.5 target threshold). The deliverable includes all required checkpoint keys: state_dict, feat_mean, feat_std, label_mean, label_std. The test verifier confirmed: 'reward = 1.0 (PASS)'. The model was evaluated on hidden test data via the external grader with metrics rel_l2≈0.27-0.30 (derived from score 0.57 interpolation), which meets the task_config.json target of 0.204 on the smoothstep curve, earning 50%+ reward.

Root causeThe agent successfully completed a challenging regression task (predicting jet-engine bracket mass from point clouds) by conducting rigorous exploratory data analysis, implementing a robust training pipeline with k-fold cross-validation to avoid overfitting on the tiny 132-sample pooled dataset, and training a PointNet model that generalized well to hidden test data. The task instructions were clear and complete, and the agent's solution correctly followed them.

RecommendationN/A - task is fine

Trajectory

Tool-by-tool agent trajectory

333 tool calls · 3 tool types · 333 steps

You are given point-cloud samples of 3D jet-engine bracket designs along with each bracket's FEA-derived mass (kg). Each point cloud is an unordered set of 2048 surface points in R^3; coordinates are pre-centered and rescaled so the axis-aligned bounding box of each bracket fits inside the unit cube. The mapping from geometry to mass is not given to you - you have to learn it from the data. Train a model that takes a `[2048, 3]` point cloud and predicts the scalar mass value. Your model is then applied to a held-out set of bracket geometries and the predictions are compared against the ground-truth FEA-simulated mass values. ## Files - `/app/data/{train,val,public_test}/samples.pt` - each file is a dict with keys `features` (shape `[N, 2048, 3]`) and `labels` (shape `[N, 1]`), both float16 on disk. Load with `torch.load(path, weights_only=True)`. - `/app/task_config.json` - architecture sizes and training defaults. - `/app/feature_schema.json` - input / label spec. - `/app/candidate_model.py` - the fixed PointNet-style architecture. Keep it as is; your saved checkpoint has to load back into this same architecture. - `/app/dataset.py` - a minimal loader for `samples.pt`. - `/app/train.py` - a small starter training loop you can extend or replace. ## What you must produce Write your trained checkpoint to `/app/outputs/model.pt` as a dict with the following required keys: - `state_dict` - your model's `state_dict()` mapping. - `feat_mean`, `feat_std` - length-3 tensors used to standardize the input coordinates before the forward pass. The same normalization is applied at inference time, so these are required. - `label_mean`, `label_std` - length-1 tensors used to standardize the target during training and de-standardize the model output at inference. The model's output is mapped back to physical units using these, so they are required too. The model must consume a `[B, 2048, 3]` tensor and produce a `[B, 1]` output. ## What you are optimizing Your predictions are measured against the true FEA-simulated mass labels with three error numbers, all lower is better: the relative L2 over the full set (the primary one), the mean absolute error, and the worst-case per-sample absolute error. A strong model needs to do well on all three, so do not trade one off against the others.

Let me start by exploring the environment to understand what I'm working with.

I'll start by exploring the environment to understand the task, the provided files, and the data.

bash

cd /app && ls -la && echo "---- TASK CONFIG ----" && cat task_config.json && echo "---- FEATURE SCHEMA ----" && cat feature_schema.json

output

total 14
drwxrws--- 3 h2tagent h2tagent  138 Jun 20 18:13 .
drwxr-xr-x 1 root     root     4096 Jun 20 20:52 ..
-rw-rw---- 1 h2tagent h2tagent 1639 Jun 20 18:10 candidate_model.py
drwxrws--- 5 h2tagent h2tagent   58 Jun 20 18:10 data
-rw-rw---- 1 h2tagent h2tagent 1183 Jun 20 18:10 dataset.py
-rw-rw---- 1 h2tagent h2tagent  499 Jun 20 18:10 feature_schema.json
-rw-rw---- 1 h2tagent h2tagent  581 Jun 20 18:10 task_config.json
-rw-rw---- 1 h2tagent h2tagent 3117 Jun 20 18:10 train.py
---- TASK CONFIG ----
{
  "task_id": "simjeb-bracket-fea-mass-prediction-real",
  "dataset_name": "simjeb-bracket-fea-mass-pilot",
  "n_points": 2048,
  "point_dim": 3,
  "label_dim": 1,
  "label_per_point": false,
  "seed": 20260518,
  "model": {
    "feature_width": 64,
    "global_width": 256,
    "head_width": 128
  },
  "training": {
    "epochs": 1,
    "batch_size": 16,
    "lr": 0.001,
    "weight_decay": 0.0001,
    "loss": "MSE"
  },
  "evaluation": {
    "primary_metric": "mass_rel_l2_score",
    "secondary_metrics": [
      "mass_mae_score",
      "mass_max_abs_err_score"
    ]
  }
}
---- FEATURE SCHEMA ----
{
  "version": "simjeb-bracket-fea-mass-pilot-v1",
  "input": {
    "type": "point_cloud",
    "n_points": 2048,
    "dim": 3,
    "coord_range": [
      -0.5,
      0.5
    ],
    "description": "Unordered surface point sample of a jet-engine bracket; each bracket mesh is centered at its bounding-box centroid and rescaled so the axis-aligned bounding box fits inside the unit cube."
  },
  "label": {
    "type": "scalar",
    "dim": 1,
    "description": "FEA-derived bracket mass in kg."
  }
}

Let me look at the model architecture, dataset loader, and training script.

Let me examine the model architecture, dataset loader, and starter training script.

bash

cd /app && echo "==== candidate_model.py ====" && cat candidate_model.py && echo "==== dataset.py ====" && cat dataset.py && echo "==== train.py ====" && cat train.py

output

==== candidate_model.py ====
from __future__ import annotations

from typing import Mapping

import torch
import torch.nn as nn


class SharedPointMLP(nn.Module):
    def __init__(self, in_dim: int, widths: tuple[int, ...]) -> None:
        super().__init__()
        layers: list[nn.Module] = []
        prev = in_dim
        for w in widths:
            layers.append(nn.Conv1d(prev, w, kernel_size=1))
            layers.append(nn.BatchNorm1d(w))
            layers.append(nn.GELU())
            prev = w
        self.net = nn.Sequential(*layers)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.net(x)


class PointNetRegressor(nn.Module):
    def __init__(self, feature_width: int, global_width: int, head_width: int) -> None:
        super().__init__()
        self.feature = SharedPointMLP(3, (feature_width, feature_width, feature_width))
        self.lift = SharedPointMLP(feature_width, (feature_width * 2, global_width))
        self.head = nn.Sequential(
            nn.Linear(global_width, head_width),
            nn.GELU(),
            nn.Linear(head_width, head_width // 2),
            nn.GELU(),
            nn.Linear(head_width // 2, 1),
        )

    def forward(self, points: torch.Tensor) -> torch.Tensor:
        x = points.transpose(1, 2).contiguous()
        x = self.feature(x)
        x = self.lift(x)
        x = x.max(dim=2).values
        return self.head(x)


def build_model(config: Mapping) -> PointNetRegressor:
    m = config["model"]
    return PointNetRegressor(
        feature_width=int(m["feature_width"]),
        global_width=int(m["global_width"]),
        head_width=int(m["head_width"]),
    )
==== dataset.py ====
from __future__ import annotations

from pathlib import Path
from typing import Iterable

import torch
from torch.utils.data import Dataset


class SampleStore(Dataset):
    def __init__(self, split_dir: str | Path) -> None:
        self.dir = Path(split_dir)
        payload = torch.load(self.dir / "samples.pt", map_location="cpu", weights_only=True)
        self.features: torch.Tensor = payload["features"].float()
        self.labels: torch.Tensor = payload["labels"].float()
        if self.features.shape[0] != self.labels.shape[0]:
            raise ValueError("features and labels must have the same number of rows")

    def __len__(self) -> int:
        return self.features.shape[0]

    def __getitem__(self, idx: int) -> tuple[torch.Tensor, torch.Tensor]:
        return self.features[idx], self.labels[idx]


def iter_batches(
    x: torch.Tensor,
    y: torch.Tensor,
    batch_size: int,
    shuffle: bool = True,
) -> Iterable[tuple[torch.Tensor, torch.Tensor]]:
    n = x.shape[0]
    idx = torch.randperm(n) if shuffle else torch.arange(n)
    for start in range(0, n, batch_size):
        chunk = idx[start : start + batch_size]
        yield x[chunk], y[chunk]
==== train.py ====
from __future__ import annotations

import json
import os
import sys
from pathlib import Path

import torch
import torch.nn.functional as F

APP_DIR = Path(os.environ.get("APP_DIR", "/app"))
sys.path.insert(0, str(APP_DIR))

from candidate_model import build_model  # noqa: E402
from dataset import SampleStore, iter_batches  # noqa: E402


def _device() -> torch.device:
    if torch.cuda.is_available():
        return torch.device("cuda")
    if torch.backends.mps.is_available():
        return torch.device("mps")
    return torch.device("cpu")


def _load_config() -> dict:
    with (APP_DIR / "task_config.json").open() as fh:
        return json.load(fh)


def main() -> None:
    cfg = _load_config()
    device = _device()
    torch.manual_seed(int(cfg["seed"]))

    train = SampleStore(APP_DIR / "data" / "train")
    val = SampleStore(APP_DIR / "data" / "val")

    feat_mean = train.features.mean(dim=(0, 1), keepdim=True)
    feat_std = train.features.std(dim=(0, 1), keepdim=True).clamp_min(1e-6)
    label_mean = train.labels.mean(dim=(0,), keepdim=True)
    label_std = train.labels.std(dim=
…[truncated]

Now let me look at the data. Let me understand the shapes and distributions.

Now let me inspect the data itself , shapes, dtypes, and label distributions across splits.

bash

cd /app && ls -la data && ls -la data/*/ && python -c "
import torch
for split in ['train','val','public_test']:
    d = torch.load(f'data/{split}/samples.pt', weights_only=True)
    f = d['features']; l = d['labels']
    print(split, 'features', f.shape, f.dtype, 'labels', l.shape, l.dtype)
    print('   label min/max/mean/std:', float(l.float().min()), float(l.float().max()), float(l.float().mean()), float(l.float().std()))
    print('   coord min/max:', float(f.float().min()), float(f.float().max()))
"

output

total 3
drwxrws--- 5 h2tagent h2tagent  58 Jun 20 18:10 .
drwxrws--- 3 h2tagent h2tagent 138 Jun 20 18:13 ..
drwxrws--- 2 h2tagent h2tagent  55 Jun 20 18:10 public_test
drwxrws--- 2 h2tagent h2tagent  55 Jun 20 18:10 train
drwxrws--- 2 h2tagent h2tagent  55 Jun 20 18:10 val
data/public_test/:
total 197
drwxrws--- 2 h2tagent h2tagent     55 Jun 20 18:10 .
drwxrws--- 5 h2tagent h2tagent     58 Jun 20 18:10 ..
-rw-rw---- 1 h2tagent h2tagent   1134 Jun 20 18:10 manifest.jsonl
-rw-rw---- 1 h2tagent h2tagent 198437 Jun 20 18:10 samples.pt

data/train/:
total 1210
drwxrws--- 2 h2tagent h2tagent      55 Jun 20 18:10 .
drwxrws--- 5 h2tagent h2tagent      58 Jun 20 18:10 ..
-rw-rw---- 1 h2tagent h2tagent    6483 Jun 20 18:10 manifest.jsonl
-rw-rw---- 1 h2tagent h2tagent 1230821 Jun 20 18:10 samples.pt

data/val/:
total 389
drwxrws--- 2 h2tagent h2tagent     55 Jun 20 18:10 .
drwxrws--- 5 h2tagent h2tagent     58 Jun 20 18:10 ..
-rw-rw---- 1 h2tagent h2tagent   2011 Jun 20 18:10 manifest.jsonl
-rw-rw---- 1 h2tagent h2tagent 395109 Jun 20 18:10 samples.pt
train features torch.Size([100, 2048, 3]) torch.float16 labels torch.Size([100, 1]) torch.float16
   label min/max/mean/std: 0.207763671875 2.21875 0.7475781440734863 0.3894648551940918
   coord min/max: -0.5 0.5
val features torch.Size([32, 2048, 3]) torch.float16 labels torch.Size([32, 1]) torch.float16
   label min/max/mean/std: 0.293701171875 2.072265625 0.7621994018554688 0.41264674067497253
   coord min/max: -0.5 0.5
public_test features torch.Size([16, 2048, 3]) torch.float16 labels torch.Size([16, 1]) torch.float16
   label min/max/mean/std: 0.1983642578125 1.44921875 0.7296066284179688 0.327779084444046
   coord min/max: -0.5 0.5

This is a small dataset: 100 training samples, 32 validation, 16 public test. The labels are masses ranging from ~0.2 to ~2.2 kg. This is a tricky regression problem with a small dataset. Let me understand the evaluation metrics. The primary metric is "mass_rel_l2_score" (relative L2), with secondary metrics MAE and max abs err. Relative L2 is typically: `||pred - true||_2 / ||true||_2` over the full set. Let me look at the manifests to understand more.

bash

cd /app && echo "---- train manifest (first 3) ----" && head -n 3 data/train/manifest.jsonl && echo "---- val manifest (first 2) ----" && head -n 2 data/val/manifest.jsonl && echo "---- public_test manifest (first 2) ----" && head -n 2 data/public_test/manifest.jsonl

output

---- train manifest (first 3) ----
{"id": "train_0000", "source_bracket_id": 214, "n_points": 2048}
{"id": "train_0001", "source_bracket_id": 538, "n_points": 2048}
{"id": "train_0002", "source_bracket_id": 546, "n_points": 2048}
---- val manifest (first 2) ----
{"id": "val_0000", "source_bracket_id": 418, "n_points": 2048}
{"id": "val_0001", "source_bracket_id": 441, "n_points": 2048}
---- public_test manifest (first 2) ----
{"id": "public_test_0000", "source_bracket_id": 461, "n_points": 2048}
{"id": "public_test_0001", "source_bracket_id": 440, "n_points": 2048}

This is the SimJEB dataset (Simulated Jet Engine Bracket). It has ~381 brackets. Here we have a pilot subset: 100 train, 32 val, 16 public test. The source_bracket_id refers to the original SimJEB bracket IDs. The key insight: mass is strongly related to the volume of the bracket. For a 3D solid object, mass = density × volume. Since all brackets are presumably the same material (density constant), mass ∝ volume. But the point clouds are surface samples, and they've been rescaled so that each bracket's bounding box fits inside the unit cube. Wait , this is critical. The rescaling is PER-BRACKET. So the absolute scale information is LOST. Each bracket is centered at its bounding-box centroid and rescaled so the axis-aligned bounding box fits inside the unit cube. Hmm, wait. Let me re-read: "coordinates are pre-centered and rescaled so the axis-aligned bounding box of each bracket fits inside the unit cube." If each bracket is independently rescaled to fit the unit cube, then the absolute size is lost, and mass would have to be inferred from shape alone (the relative geometry / topology / how much material fills the bounding box). That's a harder problem but still learnable , the shape tells you how "solid" vs "thin/hollow" the bracket is, and the aspect ratio. Actually wait, let me check: is the rescaling per-bracket with a uniform scale factor (preserving aspect ratio), or is each axis scaled independently? "rescaled so the axis-aligned bounding box of each bracket fits inside the unit cube" , and coord_range is [-0.5, 0.5]. Let me check whether each bracket actually spans the full [-0.5, 0.5] in all three dimensions (independent scaling) or just in the largest dimension (uniform scaling). Let me check the per-bracket bounding box extents.

This appears to be the SimJEB (Simulated Jet Engine Bracket) dataset. A crucial question is how the per-bracket rescaling works, since that determines whether absolute scale information survives. Let me investigate the per-bracket bounding boxes.

bash

cd /app && python -c "
import torch
d = torch.load('data/train/samples.pt', weights_only=True)
f = d['features'].float()  # [100,2048,3]
l = d['labels'].float().squeeze(1)
# per-bracket min/max per axis
mn = f.min(dim=1).values  # [100,3]
mx = f.max(dim=1).values
ext = mx - mn
print('per-axis extent stats (min over brackets):', ext.min(dim=0).values)
print('per-axis extent stats (max over brackets):', ext.max(dim=0).values)
print('per-axis extent stats (mean over brackets):', ext.mean(dim=0))
print()
# max extent per bracket (should be ~1.0 if uniform scaling to unit cube)
maxext = ext.max(dim=1).values
print('max-extent per bracket: min/max/mean', float(maxext.min()), float(maxext.max()), float(maxext.mean()))
print('how many brackets have max-extent within [0.99,1.01]:', int(((maxext>0.99)&(maxext<1.01)).sum()))
print()
# centroid of points
print('per-bracket point centroid mean (abs):', f.mean(dim=1).abs().mean(dim=0))
print('per-bracket bbox center mean:', ((mx+mn)/2).abs().mean(dim=0))
"

output

per-axis extent stats (min over brackets): tensor([0.5022, 0.9912, 0.2758])
per-axis extent stats (max over brackets): tensor([0.6436, 1.0000, 0.4550])
per-axis extent stats (mean over brackets): tensor([0.5887, 0.9991, 0.3499])

max-extent per bracket: min/max/mean 0.9912109375 1.0 0.9991015791893005
how many brackets have max-extent within [0.99,1.01]: 100

per-bracket point centroid mean (abs): tensor([0.0233, 0.0091, 0.0710])
per-bracket bbox center mean: tensor([0.0006, 0.0003, 0.0003])

So the scaling is UNIFORM (isotropic) per bracket , the max extent across all axes is always ~1.0 (the Y axis always seems to be the longest, reaching 1.0, consistently). The bounding box is centered at origin (bbox center ~0). So every bracket is scaled by a uniform factor so that its largest dimension = 1.0. This means the absolute scale is lost, BUT the aspect ratios are preserved. The Y-axis is always the longest dimension (extent ~1.0), while X is ~0.59 and Z is ~0.35 on average. So to predict mass, we cannot just use volume in the normalized frame because the true physical scale is unknown. The mass depends on: 1. The shape (how much of the bounding box is filled with material , "solidity") 2. The original absolute scale, which is LOST. Wait, but if scale is lost, how can mass be predicted at all? Unless the brackets all share a common mounting interface with fixed real-world dimensions. In the SimJEB dataset, all brackets must connect to the same 4 bolt holes and a load interface, which have FIXED physical dimensions across all designs! So the absolute scale IS encoded: since the 4 mounting holes are at fixed real-world positions, the normalized representation's scale factor can be inferred from the relative size of these interface features. Actually, more directly: the design space has a fixed mounting footprint. All brackets share the same bounding box in physical space? No , the bounding box varies per design. But the mounting interface (bolt hole locations) is fixed in physical space across all brackets. So after per-bracket normalization, the distance between bolt holes in normalized coordinates tells you the scale factor. The network can learn this from geometry. Regardless of the exact mechanism, the mapping from normalized shape → mass is learnable from data (the problem says so). The model needs to capture geometric features. Given only 100 training samples, this is a small-data regression problem, and the PointNet architecture is fixed. Let me think about the key constraints: 1. The architecture is FIXED (candidate_model.py, PointNetRegressor). I must keep it as-is and my checkpoint must load into it. The config gives feature_width=64, global_width=256, head_width=128. 2. I need to produce /app/outputs/model.pt with state_dict, feat_mean, feat_std, label_mean, label_std. 3. I'm optimizing relative L2 (primary), MAE, and max abs error. Let me reconsider whether I must use exactly the config's model sizes. The grader loads the checkpoint back "into this same architecture." The architecture is built via build_model(config) using task_config.json presumably. So I should use the same model sizes as in task_config.json: feature_width=64, global_width=256, head_width=128. If I change sizes, state_dict won't load. So I'll keep those. Let me verify how the grader likely loads the model. It will: - build_model(config) with task_config.json model sizes - load state_dict from my checkpoint - apply feat_mean/feat_std normalization to input - forward - de-standardize with label_mean/label_std So I must match that pipeline. My feat_mean/feat_std are length-3, label_mean/label_std length-1. The normalization at inference: `(x - feat_mean)/feat_std`, then model forward, then `pred*label_std + label_mean`. I should confirm the expected normalization convention matches the starter (it does: norm_x subtracts mean divides std; de-norm multiplies std adds mean). Now the main challenge: train a good model on 100 samples with a fixed PointNet. Key techniques: 1. **Data augmentation**: Since the point cloud is an unordered set and mass is invariant to permutation (PointNet handles this) and to certain transformations. What transformations preserve mass? - Permutation of points: yes invariant (PointNet max-pool). - Random point resampling/jitter: small jitter is fine. - Reflection/mirror: A bracket mirrored has the same mass. Reflections across symmetry axes. Actually the brackets likely have a symmetry plane. Mirror …[truncated]

The scaling is **uniform (isotropic)** per bracket , the longest axis (always Y) is normalized to 1.0, and the bbox is centered at the origin. So absolute scale is lost, and mass must be inferred from shape. This is the SimJEB dataset where all brackets share a fixed physical mounting interface, so scale is implicitly recoverable from geometry. Let me do some EDA to understand how predictable mass is from simple geometric features , this tells me the achievable ceiling and guides design.

bash

cd /app && python -c "
import torch, numpy as np
d = torch.load('data/train/samples.pt', weights_only=True)
f = d['features'].float().numpy()
l = d['labels'].float().squeeze(1).numpy()

# Simple geometric features per bracket
feats = {}
mn = f.min(1); mx = f.max(1); ext = mx-mn
feats['ext_x']=ext[:,0]; feats['ext_y']=ext[:,1]; feats['ext_z']=ext[:,2]
feats['bbox_vol']=ext[:,0]*ext[:,1]*ext[:,2]
feats['std_x']=f[:,:,0].std(1); feats['std_y']=f[:,:,1].std(1); feats['std_z']=f[:,:,2].std(1)
# voxel occupancy at various resolutions
for R in [8,16,24,32]:
    occ=[]
    for i in range(f.shape[0]):
        q=np.floor((f[i]+0.5)*R).clip(0,R-1).astype(int)
        vox=np.zeros((R,R,R),bool)
        vox[q[:,0],q[:,1],q[:,2]]=True
        occ.append(vox.sum()/float(R**3))
    feats[f'occ{R}']=np.array(occ)

print('Correlation of each feature with mass:')
for k,v in feats.items():
    c=np.corrcoef(v,l)[0,1]
    print(f'  {k:10s} corr={c:+.3f}')
"

output

Correlation of each feature with mass:
  ext_x      corr=+0.234
  ext_y      corr=+0.143
  ext_z      corr=+0.177
  bbox_vol   corr=+0.253
  std_x      corr=+0.635
  std_y      corr=+0.149
  std_z      corr=+0.250
  occ8       corr=+0.701
  occ16      corr=+0.775
  occ24      corr=+0.686
  occ32      corr=+0.660

Voxel occupancy at resolution 16 correlates 0.775 with mass. That's a decent single-feature correlation. Combining multiple features with a regression could do better. Let me check a multi-feature linear/MLP regression to estimate the ceiling, and also check how a nearest-neighbor in point-cloud-feature space does. Let me build a richer feature set and fit a regression (e.g., gradient boosting or ridge) with cross-validation to see how well mass can be predicted. This gives a ceiling estimate and also sanity about val/test. Actually, the key point is I must use the PointNet architecture for the final model. But understanding the ceiling helps me know how hard to push. Let me think about the relative L2 metric target. Let me estimate: if I just predict the mean mass for everything, what's the relative L2? rel_L2 = ||y - mean||/||y|| = std-ish/rms. For val: mean predictor gives rel L2 = sqrt(sum (y-ybar)^2)/sqrt(sum y^2). Let me compute baseline metrics for "predict train mean" on val and public_test. Let me also fit a strong classical model (gradient boosting on voxel + geometric features) to see achievable rel L2. This is informative. But ultimately the deliverable must be the PointNet. However , clever idea: I could make the PointNet essentially memorize/compute these voxel-like features. With enough capacity and training, PointNet can learn good geometric features. But with only 100 training samples, overfitting is a risk; I need heavy augmentation and regularization. Let me estimate the ceiling with a classical model using leave-out validation (train on train, evaluate on val and public_test).

Voxel occupancy (res 16) alone correlates 0.78 with mass. Let me estimate the achievable ceiling with a classical model on hand-crafted features, and compute baseline metrics (predicting the train mean), evaluating on val and public_test.

bash

cd /app && python -c "
import torch, numpy as np
def load(s):
    d=torch.load(f'data/{s}/samples.pt',weights_only=True)
    return d['features'].float().numpy(), d['labels'].float().squeeze(1).numpy()
def featurize(f):
    mn=f.min(1);mx=f.max(1);ext=mx-mn
    cols=[ext, f.std(1), np.abs(f).mean(1)]
    for R in [8,12,16,20,24]:
        occ=[]
        for i in range(f.shape[0]):
            q=np.floor((f[i]+0.5)*R).clip(0,R-1).astype(int)
            vox=np.zeros((R,R,R),bool); vox[q[:,0],q[:,1],q[:,2]]=True
            occ.append(vox.sum()/float(R**3))
        cols.append(np.array(occ)[:,None])
    return np.concatenate(cols,1)
Xtr,ytr=load('train'); Xv,yv=load('val'); Xp,yp=load('public_test')
Ftr=featurize(Xtr); Fv=featurize(Xv); Fp=featurize(Xp)
def metrics(pred,true):
    e=pred-true
    rel=np.sqrt((e**2).sum())/np.sqrt((true**2).sum())
    return rel, np.abs(e).mean(), np.abs(e).max()
# baseline: predict train mean
for name,(F,y) in {'val':(Fv,yv),'public':(Fp,yp)}.items():
    print(f'mean-predictor {name}: rel/mae/max=', [round(x,4) for x in metrics(np.full_like(y,ytr.mean()),y)])
# standardize feats
mu=Ftr.mean(0);sd=Ftr.std(0)+1e-9
from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor
from sklearn.linear_model import Ridge
for mdl,nm in [(Ridge(alpha=1.0),'ridge'),(RandomForestRegressor(n_estimators=400,random_state=0),'rf'),(GradientBoostingRegressor(random_state=0),'gbr')]:
    mdl.fit((Ftr-mu)/sd,ytr)
    pv=mdl.predict((Fv-mu)/sd); pp=mdl.predict((Fp-mu)/sd)
    print(f'{nm:6s} val   ',[round(x,4) for x in metrics(pv,yv)], '| public',[round(x,4) for x in metrics(pp,yp)])
" 2>/dev/null || echo "sklearn not available"

output

mean-predictor val: rel/mae/max= [np.float32(0.4706), np.float32(0.3024), np.float32(1.3247)]
mean-predictor public: rel/mae/max= [np.float32(0.3995), np.float32(0.2607), np.float32(0.7016)]
sklearn not available

sklearn is not available. Let me check what's available and install if possible, or write my own simple regressors. Let me check the environment.

Let me check the available environment and compute capabilities.

bash

cd /app && python -c "import torch; print('torch', torch.__version__, 'cuda', torch.cuda.is_available())" && nvidia-smi -L 2>/dev/null || echo "no gpu"; echo "---- CPUs ----"; nproc; echo "---- mem ----"; free -h 2>/dev/null | head -2; echo "---- pip pkgs ----"; pip list 2>/dev/null | grep -iE "numpy|scikit|scipy|torch"

output

torch 2.5.1+cu124 cuda True
GPU 0: NVIDIA H100 80GB HBM3 (UUID: GPU-a5c6efc2-7ed8-4aca-4a53-036d08db2907)
---- CPUs ----
3
---- mem ----
               total        used        free      shared  buff/cache   available
Mem:            30Gi        63Mi        29Gi          0B        29Mi        29Gi
---- pip pkgs ----
numpy                     2.1.2
torch                     2.5.1+cu124
torchaudio                2.5.1+cu124
torchelastic              0.2.2
torchvision               0.20.1+cu124

We have an H100 GPU, 3 CPUs, 30GB RAM. No sklearn but I have torch and numpy. I can implement a ceiling estimate with a small torch MLP on voxel features, or I'll just proceed to the PointNet which is what matters. Let me estimate the ceiling with a quick torch-based gradient descent ridge/MLP on handcrafted features, using train->val. Actually, let me just implement a simple k-NN regressor and a torch MLP on the handcrafted features to get a sense of the ceiling. Actually, the most important thing is to build the best PointNet. Let me not over-invest in classical ceiling. But a quick kNN and linear regression in numpy gives me a target. Let me do linear regression (closed form) and kNN quickly.

We have an H100 GPU. No sklearn, but I'll estimate the ceiling with numpy (closed-form ridge regression and kNN) on handcrafted features.

bash

cd /app && python -c "
import torch, numpy as np
def load(s):
    d=torch.load(f'data/{s}/samples.pt',weights_only=True)
    return d['features'].float().numpy(), d['labels'].float().squeeze(1).numpy()
def featurize(f):
    mn=f.min(1);mx=f.max(1);ext=mx-mn
    cols=[ext, f.std(1), np.abs(f).mean(1)]
    for R in [8,12,16,20,24]:
        occ=[]
        for i in range(f.shape[0]):
            q=np.floor((f[i]+0.5)*R).clip(0,R-1).astype(int)
            vox=np.zeros((R,R,R),bool); vox[q[:,0],q[:,1],q[:,2]]=True
            occ.append(vox.sum()/float(R**3))
        cols.append(np.array(occ)[:,None])
    return np.concatenate(cols,1)
Xtr,ytr=load('train'); Xv,yv=load('val'); Xp,yp=load('public_test')
Ftr=featurize(Xtr); Fv=featurize(Xv); Fp=featurize(Xp)
mu=Ftr.mean(0);sd=Ftr.std(0)+1e-9
A=(Ftr-mu)/sd; Av=(Fv-mu)/sd; Ap=(Fp-mu)/sd
def metrics(pred,true):
    e=pred-true; return (np.sqrt((e**2).sum())/np.sqrt((true**2).sum()), np.abs(e).mean(), np.abs(e).max())
# Ridge closed form
for lam in [0.1,1.0,10.0]:
    Aa=np.concatenate([A,np.ones((A.shape[0],1))],1)
    W=np.linalg.solve(Aa.T@Aa+lam*np.eye(Aa.shape[1]), Aa.T@ytr)
    pv=np.concatenate([Av,np.ones((Av.shape[0],1))],1)@W
    pp=np.concatenate([Ap,np.ones((Ap.shape[0],1))],1)@W
    print(f'ridge lam={lam}: val',[round(x,4) for x in metrics(pv,yv)],'| public',[round(x,4) for x in metrics(pp,yp)])
# kNN
def knn(q,k):
    d=((A[None]-q[:,None])**2).sum(-1); idx=np.argsort(d,1)[:,:k]; return ytr[idx].mean(1)
for k in [1,3,5]:
    print(f'knn k={k}: val',[round(x,4) for x in metrics(knn(Av,k),yv)],'| public',[round(x,4) for x in metrics(knn(Ap,k),yp)])
"

output

ridge lam=0.1: val [np.float64(0.2061), np.float64(0.1361), np.float64(0.553)] | public [np.float64(0.2906), np.float64(0.1965), np.float64(0.4796)]
ridge lam=1.0: val [np.float64(0.2059), np.float64(0.1336), np.float64(0.5565)] | public [np.float64(0.2794), np.float64(0.1912), np.float64(0.4467)]
ridge lam=10.0: val [np.float64(0.2161), np.float64(0.1415), np.float64(0.6529)] | public [np.float64(0.2823), np.float64(0.195), np.float64(0.3766)]
knn k=1: val [np.float32(0.2238), np.float32(0.1485), np.float32(0.4824)] | public [np.float32(0.3427), np.float32(0.1953), np.float32(0.7788)]
knn k=3: val [np.float32(0.2571), np.float32(0.1727), np.float32(0.6559)] | public [np.float32(0.3001), np.float32(0.1632), np.float32(0.6751)]
knn k=5: val [np.float32(0.2555), np.float32(0.1712), np.float32(0.6848)] | public [np.float32(0.2238), np.float32(0.1365), np.float32(0.4166)]

With simple handcrafted features + ridge, I get val rel L2 ~0.206, public ~0.28. The mean predictor gives val 0.47, public 0.40. So there's real signal. A good PointNet should be able to beat ridge on handcrafted features, since it can learn richer geometric representations. The ceiling is interesting: handcrafted features + ridge → rel L2 ~0.21 (val), ~0.28 (public). These features are crude (just occupancy + extents). A richer voxel-CNN or better features could do better. But I'm constrained to the PointNet architecture. Let me think about what determines mass physically. Mass ∝ physical volume = (normalized volume) × (scale_factor)^3. The normalized volume (occupancy) is observable. The scale_factor is the unknown physical size, which must be inferred from interface geometry. Since all brackets share the same physical mounting interface, the scale factor ≈ (physical interface size) / (normalized interface size). So if I can detect the interface features (bolt holes) and measure their normalized size, I get the scale. PointNet can in principle learn this. Given the difficulty and small data, let me aim to get the PointNet to match or beat the ridge baseline, targeting val rel L2 well under 0.20 and ideally lower. Let me think carefully about maximizing PointNet performance on this small dataset: 1. **Architecture is fixed** , I can't change widths. But I CAN control: - Input representation: the model takes [B,2048,3]. I must feed 3D coords. But I could augment the coordinates... no, input is fixed at dim 3. feat_mean/feat_std are length-3. So input must be raw (normalized) xyz. I cannot add extra input channels because the first conv is Conv1d(3, feature_width). So input is strictly 3 channels. Wait , could I transform the coordinates nonlinearly before feeding? The inference pipeline is fixed: `(x - feat_mean)/feat_std` then model. So the input transform is just standardization. I can't inject handcrafted features. The model must work from raw normalized xyz. OK. 2. **Training**: I control the entire training. I'll: - Use heavy augmentation: random reflections (8 sign-flip combos preserve mass & bbox), small jitter, random point subsampling/resampling, maybe tiny rotations about the axes (small angles), random point permutation (irrelevant for PointNet but fine). - Use MSE loss in standardized label space (aligns with rel L2). Maybe add small L1 / Huber for robustness on worst-case. - Regularize: weight decay, maybe dropout? The architecture has no dropout; I can't add it (would change state_dict? No , dropout has no params, but it's not in the architecture so I can't insert it without modifying the module. I must keep architecture as-is). So regularize via weight decay, augmentation, early stopping on val. - Train longer with cosine LR schedule. - Use an ensemble? The checkpoint loads into ONE model instance. I can only save one state_dict. So no ensemble unless I average weights. I could train multiple models and average predictions , but the checkpoint is a single model. Weight averaging (like SWA) across checkpoints of the same run is valid and loads into one model. I'll consider SWA/EMA to stabilize. 3. **BatchNorm concern**: The model uses BatchNorm1d. With batch_size=16 and 100 samples, BN statistics matter. At inference, the grader will call model.eval() presumably, using running stats. I need running stats to be well-estimated. EMA/longer training helps. Also, with heavy augmentation BN running stats reflect augmented distribution; test data is un-augmented but within distribution (reflections include identity). Should be fine. Actually, I should make sure the grader uses eval mode. I don't control that. Let me assume eval() is used (standard). The starter's val uses model.eval(). Reasonable. But there's a subtlety: if the grader does NOT call eval() and runs in train mode with batch stats... unlikely. Standard inference uses eval(). I'll ensure running stats are good. Let me …[truncated]

There's real signal: handcrafted features + ridge reach val rel-L2 ≈ 0.21 (vs 0.47 for the mean predictor). A well-trained PointNet should match or beat this. Key design insights before I write code: - **Input is strictly 3 channels** (first layer is `Conv1d(3, ...)`), and inference applies only `(x-feat_mean)/feat_std`. So I can't inject handcrafted features; the model must learn from raw xyz. - **Mass-preserving augmentations** that respect the normalization convention (bbox centered, Y-axis longest): the 8 axis sign-flips (reflections), small jitter, and point resampling. Rotations/scaling would break the bbox-normalization convention, so I'll avoid them. - **Isotropic normalization**: I'll use a single global std across all 3 axes so aspect ratios (which carry mass signal) aren't distorted. - I'll use **MSE** (directly aligned with rel-L2), plus **EMA** weights for stability, and select checkpoints on val. Let me set up a scratch workspace and write a configurable trainer.

bash

cd /app && ls outputs 2>/dev/null || echo "no outputs dir yet"; mkdir -p /app/scratch && python -c "
import torch
d=torch.load('data/train/samples.pt',weights_only=True)
f=d['features'].float()
print('global per-axis mean', f.mean(dim=(0,1)))
print('global per-axis std ', f.std(dim=(0,1)))
print('overall std (all coords)', f.std())
print('rms per axis', (f**2).mean(dim=(0,1)).sqrt())
"

output

no outputs dir yet
global per-axis mean tensor([-0.0135, 0.0075, -0.0710])
global per-axis std  tensor([0.1421, 0.2702, 0.0871])
overall std (all coords) tensor(0.1863)
rms per axis tensor([0.1428, 0.2703, 0.1124])

Now let me write a comprehensive, configurable training module. I'll put shared utilities in a file and a training script that I can invoke with different hyperparameters. Let me design the trainer: - Load splits to GPU as float32. - Normalization: feat_mean = per-axis mean (length 3), feat_std = single scalar broadcast to length 3 (isotropic). label_mean/std from train labels. - Augmentation function (applied per-batch on GPU): - random sign flip per axis (Rademacher) , 8 reflections - jitter: + noise * sigma - resample points: per-sample bootstrap/subsample - Model build, AdamW, cosine LR with warmup, grad clip. - EMA. - Eval function computing rel L2, MAE, max abs in PHYSICAL units (de-standardize). - Track best EMA and best raw by rel L2. - Save best checkpoint. Let me write it to /app/scratch/trainlib.py and a runner. Actually, I'll write one script train_dev.py that takes args (via env or argparse) and does train->val experiments, printing metrics and saving. Then a final script. Let me be careful: eval should mimic grader. Grader: model.eval(); pred = model((x-feat_mean)/feat_std); mass = pred*label_std+label_mean. Compare to true. So in eval I do exactly that. Let me write the library.

Now I'll write a configurable training library with GPU augmentation, EMA, cosine schedule, and metric tracking that mirrors the grader's inference pipeline.

str_replace_based_edit_tool

command: create
path: /app/scratch/trainlib.py
file_text: from __future__ import annotations
import json, os, sys, copy, math
from pathlib import Path
import torch
import torch.nn as nn
import torch.nn.functional as F

APP = Path("/app")
sys.path.insert(0, str(APP))
from candidate_model import build_model  # noqa

DEV = torch.device("cuda" if torch.cuda.is_available() else "cpu")


def load_split(name):
    d = torch.load(APP / "data" / name / "samples.pt", weights_only=True)
    return d["features"].float(), d["labels"].float()


def load_cfg():
    with (APP / "task_config.json").open() as fh:
        return json.load(fh)


def compute_norm(feats, labels, isotropic=True):
    feat_mean = feats.mean(dim=(0, 1))            # [3]
    if isotropic:
        s = feats.std().repeat(3)                 # single scalar across axes
    else:
        s = feats.std(dim=(0, 1))
    feat_std = s.clamp_min(1e-6)
    label_mean = labels.mean(dim=0)               # [1]
    label_std = labels.std(dim=0).clamp_min(1e-6)
    return feat_mean, feat_std, label_mean, label_std


def augment(x, *, flip=True, jitter=0.0, resample=0.0, scale=0.0, rot=0.0):
    """x: [B,N,3] on device. Returns augmented copy. Mass-preserving ops only."""
    B, N, _ = x.shape
    out = x
    if flip:
        signs = (torch.randint(0, 2, (B, 1, 3), device=x.device, dtype=x.dtype) * 2 - 1)
        out = out * signs
    if resample > 0.0:
        # per-sample: drop a random fraction and resample with replacement to N
        keep = int(round(N * (1.0 - resample)))
        idx = torch.randint(0, keep, (B, N), device=x.device)
        base = torch.stack([torch.randperm(N, device=x.device)[:keep] for _ in range(B)])
        gather_idx = torch.gather(base, 1, idx)
        out = torch.gather(out, 1, gather_idx.unsqueeze(-1).expand(-1, -1, 3))
    if scale > 0.0:
        s = 1.0 + (torch.rand(B, 1, 3, device=x.device, dtype=x.dtype) * 2 - 1) * scale
        out = out * s
    if rot > 0.0:  # small rotation about Y (vertical/long axis) only, radians
        ang = (torch.rand(B, device=x.device) * 2 - 1) * rot
        c, s = torch.cos(ang), torch.sin(ang)
        xr = out[..., 0] * c[:, None] + out[..., 2] * s[:, None]
        zr = -out[..., 0] * s[:, None] + out[..., 2] * c[:, None]
        out = torch.stack([xr, out[..., 1], zr], dim=-1)
    if jitter > 0.0:
        out = out + torch.randn_like(out) * jitter
    return out


class EMA:
    def __init__(self, model, decay=0.999):
        self.decay = decay
        self.shadow = copy.deepcopy(model).eval()
        for p in self.shadow.parameters():
            p.requires_grad_(False)

    @torch.no_grad()
    def update(self, model):
        d = self.decay
        for s, m in zip(self.shadow.state_dict().values(), model.state_dict().values()):
            if s.dtype.is_floating_point:
                s.mul_(d).add_(m.detach(), alpha=1 - d)
            else:
                s.copy_(m)


@torch.no_grad()
def evaluate(model, x, y, fmean, fstd, lmean, lstd):
    model.eval()
    xn = (x - fmean) / fstd
    preds = []
    for i in range(0, xn.shape[0], 64):
        preds.append(model(xn[i:i+64]))
    pred = torch.cat(preds, 0) * lstd + lmean   # physical units
    true = y
    e = (pred - true).squeeze(1)
    t = true.squeeze(1)
    rel = torch.sqrt((e**2).sum()) / torch.sqrt((t**2).sum())
    mae = e.abs().mean()
    mx = e.abs().max()
    return float(rel), float(mae), float(mx)


def train_run(cfg_over, train_data, val_data, probe_data=None, verbose=True):
    torch.manual_seed(cfg_over.get("seed", 0))
    cfg = load_cfg()
    xb_all, yb_all = train_data
    xb_all, yb_all = xb_all.to(DEV), yb_all.to(DEV)
    vx, vy = val_data
    vx, vy = vx.to(DEV), vy.to(DEV)

    fmean, fstd, lmean, lstd = compute_norm(xb_all, yb_all, cfg_over.get("isotropic", True))
    fmean, fstd = fmean.to(DEV), fstd.to(DEV)
    lmean, lstd = lmean.to(DEV), lstd.to(DEV)

    model = build_model(cfg).to(DEV)
    epochs = cfg_over["epochs"]
    bs = cfg_over.
…[truncated]

output

New file created successfully at /app/scratch/trainlib.py

Now let me run a first experiment: baseline MSE with flip augmentation, moderate epochs, and see val/probe behavior. I'll write a quick runner.

Let me run a first experiment to understand training dynamics: MSE loss with reflection augmentation, comparing raw vs EMA weights, and probing public_test as a secondary generalization check.

bash

cd /app/scratch && python -c "
import torch, trainlib as T
tr = T.load_split('train'); va = T.load_split('val'); pt = T.load_split('public_test')
cfg = dict(seed=0, epochs=400, bs=16, lr=1.5e-3, wd=1e-4, warmup=20, ema_decay=0.999,
           loss='mse', isotropic=True, eval_every=40,
           aug=dict(flip=True, jitter=0.01, resample=0.15))
best = T.train_run(cfg, tr, va, probe_data=pt)
print('BEST val rel/mae/mx=', round(best['rel'],4), round(best['mae'],4), round(best['mx'],4), 'via', best['which'], 'ep', best['ep'])
"

output

ep  40 raw=0.392/0.297/0.844 ema=0.471/0.300/1.332 | probe(ema) rel/mae/mx=0.399/0.260/0.709  <== best raw
ep  80 raw=0.332/0.203/0.971 ema=0.472/0.297/1.345 | probe(ema) rel/mae/mx=0.399/0.259/0.722  <== best raw
ep 120 raw=0.288/0.198/0.538 ema=0.475/0.295/1.365 | probe(ema) rel/mae/mx=0.400/0.259/0.742  <== best raw
ep 160 raw=0.279/0.190/0.575 ema=0.480/0.296/1.391 | probe(ema) rel/mae/mx=0.404/0.259/0.768  <== best raw
ep 200 raw=0.230/0.155/0.470 ema=0.488/0.297/1.421 | probe(ema) rel/mae/mx=0.411/0.259/0.800  <== best raw
ep 240 raw=0.264/0.181/0.514 ema=0.497/0.298/1.446 | probe(ema) rel/mae/mx=0.422/0.260/0.831
ep 280 raw=0.225/0.141/0.553 ema=0.507/0.298/1.466 | probe(ema) rel/mae/mx=0.437/0.264/0.861  <== best raw
ep 320 raw=0.248/0.161/0.639 ema=0.521/0.306/1.480 | probe(ema) rel/mae/mx=0.458/0.278/0.894
ep 360 raw=0.248/0.167/0.569 ema=0.533/0.312/1.466 | probe(ema) rel/mae/mx=0.480/0.300/0.923
ep 400 raw=0.258/0.172/0.639 ema=0.534/0.313/1.401 | probe(ema) rel/mae/mx=0.496/0.316/0.941
BEST val rel/mae/mx= 0.2247 0.1407 0.553 via raw ep 280

Interesting. A few observations: 1. The EMA is WORSE than raw and barely moves , it's stuck near the mean predictor (rel 0.47). This means EMA decay 0.999 over only ~6 steps/epoch × 400 = 2400 steps is too slow to catch up; the shadow is dominated by early (near-init) weights. Actually decay 0.999 means effective window ~1000 steps. With 2400 steps it should catch up... but it starts from the initial model and the running BN stats in the shadow may be off. Actually the issue: EMA.shadow's BatchNorm running stats , I copy them via state_dict including running_mean/var. But I update the shadow's BN running stats with EMA of the model's BN running stats (non-floating? they are floating). Actually BN running stats are floating, so I EMA them too. But BN num_batches_tracked is a long (non-float) , I copy it. Hmm. The real issue: EMA rel ~0.47 = mean predictor. The EMA model basically predicts constant. Possibly because EMA weights average over very different weight configs → washes out. Or the EMA BN stats don't match EMA weights. Let me not rely on EMA with this decay; raw model does better. 2. The raw model reaches best val rel ~0.225 around ep 280, but it's noisy and then degrades (overfitting). Probe (public) with EMA stays ~0.40 (since EMA ~ mean predictor). Not informative because EMA is bad. 3. The best val rel 0.225 is similar to ridge baseline (~0.206). The jitter=0.01 and resample=0.15 may be limiting. Let me reconsider. Issues to fix: - EMA is ineffective. Let me reduce EMA decay (e.g., 0.99 or 0.95) so it tracks better, OR re-estimate BN stats for the EMA model before eval. Actually a cleaner approach: after training, recompute BN running statistics by doing forward passes in train() mode over the (augmented) training data for the final weights , but for EMA that's complex. Let me instead reduce reliance on EMA and use best raw checkpoint via val selection. But selecting best raw on val (32 samples) with noisy eval every 40 epochs is coarse. Let me reconsider the whole approach. The val curve is noisy and overfits. I need stronger regularization to get a stable low val rel. Options: - More aggressive augmentation (more flips always on, higher jitter, resample). - Lower learning rate / more epochs with cosine to converge smoothly. - Weight decay higher. But also: the raw model eventually overfits (train loss →0). With only 100 samples and a model with ~? params. Let me count params. Let me also reconsider: maybe EMA would be fine if I evaluate it with proper BN stats. Let me debug EMA. Actually the simplest robust method: use EMA but with BN stats recomputed, OR just track the best raw checkpoint by evaluating EVERY epoch (cheap) and keep the best on val. Since val is only 32 samples and eval is fast, evaluate every epoch and keep best. But best-on-val selection with 32 samples may overfit to val. Still, it's my signal. Let me improve: 1. Evaluate every epoch (cheap), track best raw by val rel L2 (and maybe by a combined metric). 2. Fix EMA: recompute BN stats for shadow before eval, or lower decay. Let me test lower decay 0.99. Actually, let me reconsider EMA BN handling. The standard approach: EMA the parameters, but for BN buffers either EMA them or copy. The problem of EMA≈mean-predictor suggests the averaged weights produce a near-constant output. That can happen if weights from many epochs average to something that, combined with BN, collapses. Hmm. Let me instead try: EMA only parameters, and copy BN buffers directly from the live model (not EMA). And use the live model's BN stats. Actually simpler: before evaluating EMA, run a few train-mode forward passes over training data with the EMA weights to recompute BN running stats. Let me implement a "bn_recalibrate" function. Actually, I realize the bigger issue. Let me step back and think about what gives the best generalization on this tiny dataset. With 100 training points, the PointNet (let me count params ~ maybe 200k) will overfit. Heavy augmentation …[truncated]

Two issues: EMA is stuck near the mean-predictor (likely BN running-stat mismatch in the shadow), and the raw model overfits after ~epoch 280. Let me check the parameter count and fix the EMA by recalibrating BatchNorm statistics.

bash

cd /app/scratch && python -c "
import trainlib as T
m = T.build_model(T.load_cfg())
n = sum(p.numel() for p in m.parameters())
print('total params:', n)
for nm,p in m.named_parameters():
    print(f'  {nm:30s} {tuple(p.shape)}')
" | head -40

output

total params: 92289
  feature.net.0.weight           (64, 3, 1)
  feature.net.0.bias             (64,)
  feature.net.1.weight           (64,)
  feature.net.1.bias             (64,)
  feature.net.3.weight           (64, 64, 1)
  feature.net.3.bias             (64,)
  feature.net.4.weight           (64,)
  feature.net.4.bias             (64,)
  feature.net.6.weight           (64, 64, 1)
  feature.net.6.bias             (64,)
  feature.net.7.weight           (64,)
  feature.net.7.bias             (64,)
  lift.net.0.weight              (128, 64, 1)
  lift.net.0.bias                (128,)
  lift.net.1.weight              (128,)
  lift.net.1.bias                (128,)
  lift.net.3.weight              (256, 128, 1)
  lift.net.3.bias                (256,)
  lift.net.4.weight              (256,)
  lift.net.4.bias                (256,)
  head.0.weight                  (128, 256)
  head.0.bias                    (128,)
  head.2.weight                  (64, 128)
  head.2.bias                    (64,)
  head.4.weight                  (1, 64)
  head.4.bias                    (1,)

92k params, 100 samples. Definitely overfitting-prone. Heavy regularization needed. Let me fix EMA with BN recalibration and evaluate every epoch. Let me add a bn_recalibrate function and per-epoch eval with best tracking. Let me update trainlib. Actually, let me reconsider the EMA problem more carefully. The shadow is a deepcopy of the model at init. I EMA all floating buffers including BN running_mean/running_var. At init, BN running_mean=0, running_var=1. As training proceeds, the live model's running_mean/var evolve. The shadow EMAs toward them with decay 0.999 → very slow, so shadow BN stats stay near 0/1 for a long time while shadow weights also lag. The combination → poor. The fix: recalibrate BN stats of the shadow by forward passes over training data (in train mode, which updates running stats) right before eval. This decouples BN stats from the EMA lag. Let me implement: copy shadow, set BN momentum to None (cumulative) or just reset running stats and do several forward passes over training set to estimate them. Let me implement bn_recalibrate(model, data_x_normalized): set model.train(), reset BN running stats, run forward over data in chunks, then eval. Actually resetting then doing one pass over all 100 training samples (normalized, no aug or with aug?) gives BN stats matching that distribution. Test data is un-augmented, so calibrate with un-augmented training data (just normalized). Let me do that. Let me rewrite the eval to optionally recalibrate BN on a provided calibration set. Let me also reconsider: maybe EMA isn't needed if I just pick best raw on val. But EMA typically generalizes better. Let me get it working and compare. Let me update trainlib with bn_recalibrate and per-epoch evaluation, and return both best-raw and best-ema (with recalibrated BN). Also I'll store the full history to pick robustly. Let me refine.

Only 92k params but just 100 samples , overfitting is the core risk. Let me fix the EMA (recalibrate BatchNorm running stats on clean training data before evaluating), evaluate every epoch, and track the best checkpoint robustly.

str_replace_based_edit_tool

command: str_replace
path: /app/scratch/trainlib.py
old_str: @torch.no_grad()
def evaluate(model, x, y, fmean, fstd, lmean, lstd):
new_str: @torch.no_grad()
def bn_recalibrate(model, x_norm, passes=3, bs=32):
    """Reset BN running stats and re-estimate them from clean normalized data."""
    for m in model.modules():
        if isinstance(m, nn.BatchNorm1d):
            m.reset_running_stats()
            m.momentum = None  # cumulative average
    model.train()
    for _ in range(passes):
        for i in range(0, x_norm.shape[0], bs):
            model(x_norm[i:i+bs])
    model.eval()
    return model


@torch.no_grad()
def evaluate(model, x, y, fmean, fstd, lmean, lstd):

output

The file /app/scratch/trainlib.py has been edited successfully.

str_replace_based_edit_tool

command: str_replace
path: /app/scratch/trainlib.py
old_str:         if (ep + 1) % cfg_over.get("eval_every", 25) == 0 or ep == epochs - 1:
            rr = evaluate(model, vx, vy, fmean, fstd, lmean, lstd)
            re = evaluate(ema.shadow, vx, vy, fmean, fstd, lmean, lstd)
            tag = ""
            for nm, m in [("raw", rr), ("ema", re)]:
                if m[0] < best["rel"]:
                    best = {"rel": m[0], "mae": m[1], "mx": m[2], "which": nm, "ep": ep+1,
                            "sd": copy.deepcopy((model if nm == "raw" else ema.shadow).state_dict())}
                    tag = "  <== best " + nm
            if verbose:
                pr = ""
                if probe_data is not None:
                    px, py = probe_data
                    pm = evaluate(ema.shadow, px.to(DEV), py.to(DEV), fmean, fstd, lmean, lstd)
                    pr = f" | probe(ema) rel/mae/mx={pm[0]:.3f}/{pm[1]:.3f}/{pm[2]:.3f}"
                print(f"ep{ep+1:4d} raw={rr[0]:.3f}/{rr[1]:.3f}/{rr[2]:.3f} "
                      f"ema={re[0]:.3f}/{re[1]:.3f}/{re[2]:.3f}{pr}{tag}", flush=True)
new_str:         ev = cfg_over.get("eval_every", 1)
        if (ep + 1) % ev == 0 or ep == epochs - 1:
            rr = evaluate(model, vx, vy, fmean, fstd, lmean, lstd)
            # recalibrate EMA BN stats on clean train data, then eval
            shadow = copy.deepcopy(ema.shadow)
            bn_recalibrate(shadow, xn_all)
            re = evaluate(shadow, vx, vy, fmean, fstd, lmean, lstd)
            tag = ""
            for nm, m, mdl in [("raw", rr, model), ("ema", re, shadow)]:
                score = m[0]  # rel L2 primary
                if score < best["rel"]:
                    best = {"rel": m[0], "mae": m[1], "mx": m[2], "which": nm, "ep": ep+1,
                            "sd": copy.deepcopy(mdl.state_dict())}
                    tag = "  <== best " + nm
            if verbose and ((ep + 1) % cfg_over.get("log_every", 40) == 0 or ep == epochs - 1):
                pr = ""
                if probe_data is not None:
                    px, py = probe_data
                    pm = evaluate(shadow, px.to(DEV), py.to(DEV), fmean, fstd, lmean, lstd)
                    pr = f" | probe(ema) {pm[0]:.3f}/{pm[1]:.3f}/{pm[2]:.3f}"
                print(f"ep{ep+1:4d} raw={rr[0]:.3f}/{rr[1]:.3f}/{rr[2]:.3f} "
                      f"ema={re[0]:.3f}/{re[1]:.3f}/{re[2]:.3f}{pr}{tag}", flush=True)

output

The file /app/scratch/trainlib.py has been edited successfully.

Now let me rerun the experiment with per-epoch eval and EMA BN recalibration, and also try a couple of EMA decays. Let me also add the ability to evaluate the EMA with different decays. For now, run with decay 0.999 and see if recalibrated EMA is better. Let me run a moderate experiment.

Now let me rerun with the fixed EMA (BN-recalibrated) and per-epoch best tracking.

bash

cd /app/scratch && python -c "
import torch, trainlib as T
tr = T.load_split('train'); va = T.load_split('val'); pt = T.load_split('public_test')
cfg = dict(seed=0, epochs=400, bs=16, lr=1.5e-3, wd=1e-4, warmup=20, ema_decay=0.997,
           loss='mse', isotropic=True, eval_every=1, log_every=50,
           aug=dict(flip=True, jitter=0.01, resample=0.15))
best = T.train_run(cfg, tr, va, probe_data=pt)
print('BEST val rel/mae/mx=', round(best['rel'],4), round(best['mae'],4), round(best['mx'],4), 'via', best['which'], 'ep', best['ep'])
"

output

ep  50 raw=0.343/0.215/0.966 ema=0.405/0.288/0.978 | probe(ema) 0.315/0.200/0.449
ep 100 raw=0.302/0.198/0.809 ema=0.365/0.244/0.824 | probe(ema) 0.279/0.170/0.575
ep 150 raw=0.289/0.188/0.824 ema=0.357/0.233/0.785 | probe(ema) 0.348/0.187/0.883
ep 200 raw=0.252/0.170/0.563 ema=0.427/0.263/1.124 | probe(ema) 0.460/0.214/1.206
ep 250 raw=0.243/0.148/0.713 ema=0.473/0.283/1.266 | probe(ema) 0.499/0.228/1.285  <== best raw
ep 300 raw=0.251/0.146/0.780 ema=0.476/0.278/1.311 | probe(ema) 0.513/0.241/1.307
ep 350 raw=0.256/0.171/0.651 ema=0.464/0.268/1.283 | probe(ema) 0.484/0.227/1.276
ep 400 raw=0.256/0.166/0.716 ema=0.450/0.257/1.264 | probe(ema) 0.457/0.213/1.230
BEST val rel/mae/mx= 0.2361 0.1488 0.651 via raw ep 298

The recalibrated EMA now tracks better early (ep50-150) with probe rel ~0.28-0.31, but then diverges as the raw weights overfit. The EMA with recalibrated BN at ep 100 gave probe 0.279 and val 0.365. Hmm. The issue: after ~ep 150 the raw model overfits, and EMA follows it up. Interesting: around ep 100-150, the EMA probe (public) is 0.28, val ema 0.36. The raw at ep 250 has val 0.243 but probe diverges (the probe with ema at ep250 is 0.499 , bad). Wait the probe is evaluated with EMA shadow, which at ep250 is overfit. The core problem: the model overfits the 100 training samples, and both val and public_test degrade. Best val (0.236) is at ep298 but that may be a lucky fit to val's 32 samples; public at that point is bad. I need MUCH stronger regularization to prevent overfitting. Let me think about what's happening: train loss probably goes near 0. The model memorizes. Augmentation (flip×8, jitter, resample) isn't enough. Ideas to regularize harder: 1. Stronger jitter (0.02-0.03). 2. Stronger resample (drop 30-50%). 3. Higher weight decay (1e-3 to 1e-2). 4. Lower LR, fewer epochs (early stop ~ep100-150 region where both val and probe are decent). 5. Reduce effective capacity: I can't change architecture, but I can add input noise and use fewer points at train. Actually the most important realization: The model at ep 50-150 has val ~0.30-0.34 and probe ~0.28-0.31, which is consistent and decent. The "best val 0.236 at ep298" is overfitting to val , probe is terrible there (0.5+). So selecting purely on val's noisy 32 samples is misleading. The generalization sweet spot is earlier, where val and probe agree (~0.30 / ~0.28). So I should NOT chase the lowest val; I should find the regime where val AND probe are both low and stable , that reflects true generalization. The ridge baseline got val 0.206, probe 0.28. The PointNet early-stopped gets val ~0.30, probe ~0.28. So currently PointNet ≈ ridge on probe but worse on val. I think I need to regularize so that the model generalizes better overall, pushing both val and probe down together. Let me try: - weight decay 3e-3 - jitter 0.02, resample 0.3 - lr 1e-3, epochs ~200 with cosine - ema decay 0.99 (faster tracking) but rely on early stopping via combined val+probe. Actually, I shouldn't use probe for selection (it's a peek at a held-out set; the real test is hidden). But I can use it to understand generalization and to choose hyperparameters that make val a RELIABLE proxy. The cleanest unbiased approach: use cross-validation over the 132 train+val samples to select hyperparameters and epoch count, then train final on all data. Given the noise with 32-sample val, let me implement k-fold CV on the combined train+val (132 samples) to get robust estimates. That's more reliable than a single 32-sample val. Let me do 4-fold or 6-fold CV. With ~130 samples and fast training on H100, CV is feasible. Let me restructure: a function that, given hyperparameters, runs K-fold CV over a pooled dataset and returns mean val metrics across folds (using best-epoch or fixed-epoch). This gives robust hyperparameter selection. Then I train the final model on all available data. But CV with "best epoch per fold" still needs an early-stopping criterion. Better: for each candidate (hyperparams + fixed epoch budget E), evaluate CV metric at the FINAL epoch E (no peeking per-fold selection), i.e., train E epochs and measure. Then pick the (hyperparams, E) with best mean CV rel L2. This avoids per-fold selection bias. Then retrain on all data for E epochs. This is the right methodology. Let me implement pooled CV. Let me think about compute: each fold trains ~150-300 epochs on ~110 samples (~7 steps/epoch) → ~1000-2000 steps, each step tiny (bs16, 2048 pts, 92k params) on H100. Very fast, maybe a few seconds per fold. 6 folds × several configs = manageable. Let me restructure trainlib to support pooled CV with fixed epoch evaluation (evaluate at several epoch checkpoints to pick E too). I'll …[truncated]

The lowest val point (ep 298) is overfitting to val's 32 samples , public_test degrades badly there, while epochs 50–150 show val and public_test agreeing (~0.30 / ~0.28). So single-split selection on 32 samples is unreliable, and I'm overfitting. The right methodology for such a tiny dataset is **k-fold cross-validation** over the pooled train+val (132 samples) to choose hyperparameters and a fixed epoch budget without per-fold cherry-picking, then retrain on all data. Let me build a proper pooled-CV harness that records snapshots at multiple epochs and computes set-level rel-L2 over out-of-fold predictions.

str_replace_based_edit_tool

command: str_replace
path: /app/scratch/trainlib.py
old_str: def save_ckpt(path, sd, norm):
new_str: def metrics_vec(pred, true):
    e = pred - true
    rel = float(torch.sqrt((e**2).sum()) / torch.sqrt((true**2).sum()))
    return rel, float(e.abs().mean()), float(e.abs().max())


def train_core(cfg_over, Xtr, Ytr, Xte, snapshot_epochs, calib_x=None):
    """Train on (Xtr,Ytr); at each snapshot epoch, predict Xte with raw+ema(recal).
    Returns dict epoch -> {'raw':pred[Nte,1], 'ema':pred[Nte,1]} in physical units."""
    torch.manual_seed(cfg_over.get("seed", 0))
    cfg = load_cfg()
    Xtr, Ytr, Xte = Xtr.to(DEV), Ytr.to(DEV), Xte.to(DEV)
    fmean, fstd, lmean, lstd = compute_norm(Xtr, Ytr, cfg_over.get("isotropic", True))
    fmean, fstd = fmean.to(DEV), fstd.to(DEV); lmean, lstd = lmean.to(DEV), lstd.to(DEV)
    calib = (Xtr if calib_x is None else calib_x.to(DEV))
    calib_n = (calib - fmean) / fstd

    model = build_model(cfg).to(DEV)
    epochs = cfg_over["epochs"]; bs = cfg_over.get("bs", 16)
    lr = cfg_over.get("lr", 1e-3); wd = cfg_over.get("wd", 1e-4)
    warm = cfg_over.get("warmup", max(1, epochs // 20)); aug = cfg_over.get("aug", {})
    loss_kind = cfg_over.get("loss", "mse"); huber_beta = cfg_over.get("huber_beta", 0.1)
    opt = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=wd)
    ema = EMA(model, cfg_over.get("ema_decay", 0.999))
    N = Xtr.shape[0]; steps_per = max(1, math.ceil(N / bs)); total = epochs * steps_per

    def lr_at(st):
        if st < warm * steps_per:
            return lr * (st + 1) / (warm * steps_per)
        p = (st - warm * steps_per) / max(1, total - warm * steps_per)
        return lr * 0.5 * (1 + math.cos(math.pi * p))

    yn_all = (Ytr - lmean) / lstd
    snaps = {}
    step = 0
    snapset = set(snapshot_epochs)
    for ep in range(epochs):
        model.train()
        perm = torch.randperm(N, device=DEV)
        for s in range(0, N, bs):
            idx = perm[s:s+bs]
            xb = augment(Xtr[idx], **aug)
            xbn = (xb - fmean) / fstd
            for g in opt.param_groups:
                g["lr"] = lr_at(step)
            opt.zero_grad(set_to_none=True)
            pred = model(xbn)
            yb = yn_all[idx]
            if loss_kind == "mse":
                loss = F.mse_loss(pred, yb)
            elif loss_kind == "huber":
                loss = F.smooth_l1_loss(pred, yb, beta=huber_beta)
            elif loss_kind == "mse+l1":
                loss = F.mse_loss(pred, yb) + 0.3 * F.l1_loss(pred, yb)
            loss.backward()
            nn.utils.clip_grad_norm_(model.parameters(), 5.0)
            opt.step(); ema.update(model); step += 1
        if (ep + 1) in snapset:
            with torch.no_grad():
                model.eval()
                xten = (Xte - fmean) / fstd
                pr = torch.cat([model(xten[i:i+64]) for i in range(0, xten.shape[0], 64)], 0)
                pr = pr * lstd + lmean
                shadow = copy.deepcopy(ema.shadow); bn_recalibrate(shadow, calib_n)
                pe = torch.cat([shadow(xten[i:i+64]) for i in range(0, xten.shape[0], 64)], 0)
                pe = pe * lstd + lmean
            snaps[ep + 1] = {"raw": pr.cpu(), "ema": pe.cpu()}
    return snaps, (fmean.cpu(), fstd.cpu(), lmean.cpu(), lstd.cpu())


def run_cv(cfg_over, pooled, n_folds, snapshot_epochs, seed=0):
    X, Y = pooled
    N = X.shape[0]
    g = torch.Generator().manual_seed(seed)
    perm = torch.randperm(N, generator=g)
    folds = [perm[i::n_folds] for i in range(n_folds)]
    # accumulate OOF predictions per (epoch, which)
    oof = {e: {"raw": torch.zeros(N, 1), "ema": torch.zeros(N, 1)} for e in snapshot_epochs}
    for k in range(n_folds):
        te_idx = folds[k]
        tr_mask = torch.ones(N, dtype=torch.bool); tr_mask[te_idx] = False
        Xtr, Ytr = X[tr_mask], Y[tr_mask]
        Xte = X[te_idx]
        snaps, _ = train_core(cfg_over, Xtr, Ytr, Xte, snapshot_epochs)
        for e in snapshot_epochs:
            oof
…[truncated]

output

The file /app/scratch/trainlib.py has been edited successfully.

Now let me run a CV to compare configs. I'll pool train+val (132 samples), 6 folds, and evaluate at snapshot epochs [50,100,150,200,250,300]. I'll test a baseline config and then vary regularization. Let me start with a moderate config and see the epoch curve. Let me write a runner that tries a few configs and prints the CV results. To keep runtime manageable, I'll start with one config across snapshots.

Now let me run 6-fold CV on pooled train+val (132 samples), recording snapshots across epochs to find the generalization sweet spot and compare raw vs EMA.

bash

cd /app/scratch && python -c "
import torch, trainlib as T, time
trX,trY=T.load_split('train'); vaX,vaY=T.load_split('val')
X=torch.cat([trX,vaX],0); Y=torch.cat([trY,vaY],0)
print('pooled', X.shape, Y.shape)
snaps=[50,100,150,200,250,300]
cfg = dict(seed=0, epochs=300, bs=16, lr=1.5e-3, wd=1e-4, warmup=20, ema_decay=0.997,
           loss='mse', isotropic=True, aug=dict(flip=True, jitter=0.01, resample=0.15))
t=time.time()
res=T.run_cv(cfg, (X,Y), n_folds=6, snapshot_epochs=snaps, seed=0)
print(f'elapsed {time.time()-t:.1f}s')
for e in snaps:
    for w in ['raw','ema']:
        r=res[(e,w)]; print(f'  ep{e:4d} {w}: rel={r[0]:.4f} mae={r[1]:.4f} mx={r[2]:.4f}')
"

output

pooled torch.Size([132, 2048, 3]) torch.Size([132, 1])
elapsed 85.4s
  ep  50 raw: rel=0.3609 mae=0.2269 mx=1.0412
  ep  50 ema: rel=0.4064 mae=0.2784 mx=1.1889
  ep 100 raw: rel=0.3509 mae=0.2226 mx=0.9302
  ep 100 ema: rel=0.3838 mae=0.2666 mx=0.8227
  ep 150 raw: rel=0.3429 mae=0.2119 mx=0.8860
  ep 150 ema: rel=0.3665 mae=0.2365 mx=1.1594
  ep 200 raw: rel=0.3222 mae=0.2065 mx=0.8145
  ep 200 ema: rel=0.3884 mae=0.2337 mx=1.3597
  ep 250 raw: rel=0.2903 mae=0.1886 mx=0.7459
  ep 250 ema: rel=0.4154 mae=0.2378 mx=1.4200
  ep 300 raw: rel=0.2952 mae=0.1950 mx=0.8137
  ep 300 ema: rel=0.4298 mae=0.2424 mx=1.3895

So the honest CV estimate (OOF over 132 samples) is rel ~0.29-0.36, much worse than the single-split val 0.236 (which was overfit to val). This is the real generalization level. Ridge got ~0.21 val / 0.28 public. So this PointNet config (~0.29 CV) is roughly comparable to ridge but not clearly better. EMA is worse here (likely BN recalibration + fast overfitting). The raw model improves with more epochs up to ~250. Let me explore: 1. Stronger regularization to push CV rel lower. 2. Longer training to see if raw keeps improving. The fact that EMA is consistently worse suggests the EMA averaging with this decay captures overfit late weights or the BN recalibration interacts poorly. Let me focus on raw with good early stopping + regularization, and reconsider EMA later. Key question: can I beat ridge's ~0.21? The handcrafted ridge used voxel occupancy features , very informative. PointNet should be able to learn similar or better features, but with 132 samples it's data-starved. Let me push regularization and training. Let me try a sweep of configs via CV. Given each CV takes ~85s for 300 epochs×6 folds, I can run several. Let me parallelize conceptually by testing key knobs: Config variations to try: A) Higher weight decay (1e-3, 3e-3) B) Stronger aug (jitter 0.02, resample 0.3) C) Longer epochs (up to 500) to see raw trajectory D) Different LR Let me run a few in one script, printing best epoch per config. To save time, I'll reduce folds to 5 and snapshots focused. Actually let me keep 6 folds for stability but test multiple configs sequentially. Each ~85s; 5 configs ~7min. Acceptable. Let me test these configs and look at the best CV rel across snapshots: 1. base (above): wd1e-4, jit0.01, rs0.15 → best 0.290@250 2. wd1e-3, jit0.02, rs0.25 3. wd3e-3, jit0.02, rs0.3 4. wd1e-3, jit0.03, rs0.35, epochs500 5. lr1e-3, wd1e-3, jit0.02, rs0.25, epochs400 Let me write a sweep runner. Actually, let me reconsider the EMA. The EMA being worse is surprising. Possibly because I deepcopy ema.shadow each eval and recalibrate , that's fine. But EMA of 0.997 over ~770 steps (110 samples/16≈7 steps × 300 ep = 2100 steps)... decay 0.997 → window ~333 steps ≈ 47 epochs. So EMA lags by ~47 epochs. At ep250 raw is near best; EMA reflects ep~200 weights which are also decent (raw 0.32). But EMA rel is 0.415 , worse than raw@200 (0.32). So EMA averaging itself hurts here, not just lag. Weight averaging across the noisy trajectory may not align with BN. Since recalibrating BN helps match, but the averaged conv weights may produce a flatter/regressed-to-mean output → higher rel but maybe lower variance. The mae for EMA isn't terrible (0.23) but rel and max are worse, meaning EMA underpredicts the heavy brackets (regression to mean). That hurts rel L2 (dominated by large masses) and max error. So for THIS metric (rel L2 dominated by large-mass samples), I want the model to NOT regress heavy brackets to the mean. EMA smoothing causes mean regression → bad. So I'll drop EMA and use raw with early stopping. Or use a light EMA (decay 0.95, short window) mainly for noise reduction. Let me just focus on raw. Let me also reconsider the loss. rel L2 is dominated by large-mass samples (big denominator but also big errors). Actually the heavy brackets (mass 2.2) are rare and the model tends to underpredict them (regression to mean), causing large errors that dominate both rel L2 numerator and max-abs. To fix, I could weight the loss to emphasize heavy samples, or predict in a way that doesn't regress to mean. Weighting by mass could help rel L2 and max-abs. Let me consider a weighted MSE where weight ∝ mass (or mass^2) to match the rel L2 emphasis. Actually rel L2 numerator = sum e_i^2; it doesn't inherently weight by mass , it's unweighted squared error, normalized by constant ||y||. So plain MSE already matches the numerator. The issue is just that heavy samples are underpredicted because they're rare. Weighting heavy samples more would reduce their …[truncated]

The honest CV estimate (~0.29–0.36 rel-L2) is much worse than the overfit single-split val (0.236), confirming that 32-sample selection was misleading. EMA regresses heavy brackets toward the mean (hurting rel-L2 and max-error), so I'll de-emphasize it. Let me add optional mass-weighting to the loss and sweep regularization strength. First, let me see which samples drive the worst errors.

bash

cd /app/scratch && python -c "
import torch, trainlib as T
trX,trY=T.load_split('train'); vaX,vaY=T.load_split('val')
X=torch.cat([trX,vaX],0); Y=torch.cat([trY,vaY],0)
cfg = dict(seed=0, epochs=250, bs=16, lr=1.5e-3, wd=1e-4, warmup=20, ema_decay=0.997,
           loss='mse', isotropic=True, aug=dict(flip=True, jitter=0.01, resample=0.15))
# single CV, collect OOF raw preds at ep250
res=T.run_cv.__wrapped__ if hasattr(T.run_cv,'__wrapped__') else None
# replicate run_cv to get oof vector
import trainlib
N=X.shape[0]; g=torch.Generator().manual_seed(0); perm=torch.randperm(N,generator=g)
folds=[perm[i::6] for i in range(6)]
oof=torch.zeros(N,1)
for k in range(6):
    te=folds[k]; m=torch.ones(N,bool); m[te]=False
    snaps,_=trainlib.train_core(cfg,X[m],Y[m],X[te],[250])
    oof[te]=snaps[250]['raw']
err=(oof-Y).squeeze(1).abs()
order=torch.argsort(err,descending=True)
print('Top-10 abs errors (true_mass, pred, abserr):')
for i in order[:10]:
    print(f'  true={float(Y[i,0]):.3f} pred={float(oof[i,0]):.3f} err={float(err[i]):.3f}')
print('mass vs err corr:', float(torch.corrcoef(torch.stack([Y.squeeze(1),err]))[0,1]))
import numpy as np
yb=Y.squeeze(1).numpy(); eb=err.numpy()
for lo,hi in [(0,0.5),(0.5,0.9),(0.9,1.3),(1.3,3)]:
    msk=(yb>=lo)&(yb<hi); print(f'  mass[{lo},{hi}): n={msk.sum():3d} meanerr={eb[msk].mean():.3f}')
"

output

[stderr]
Traceback (most recent call last):
  File "<string>", line 15, in <module>
TypeError: ones(): argument 'size' failed to unpack the object at pos 2 with error "type must be tuple of ints,but got type"

Minor bug: torch.ones(N, bool) should be torch.ones(N, dtype=torch.bool). Let me fix.

bash

cd /app/scratch && python -c "
import torch, trainlib
trX,trY=trainlib.load_split('train'); vaX,vaY=trainlib.load_split('val')
X=torch.cat([trX,vaX],0); Y=torch.cat([trY,vaY],0)
cfg = dict(seed=0, epochs=250, bs=16, lr=1.5e-3, wd=1e-4, warmup=20, loss='mse', isotropic=True,
           aug=dict(flip=True, jitter=0.01, resample=0.15))
N=X.shape[0]; g=torch.Generator().manual_seed(0); perm=torch.randperm(N,generator=g)
folds=[perm[i::6] for i in range(6)]
oof=torch.zeros(N,1)
for k in range(6):
    te=folds[k]; m=torch.ones(N,dtype=torch.bool); m[te]=False
    snaps,_=trainlib.train_core(cfg,X[m],Y[m],X[te],[250])
    oof[te]=snaps[250]['raw']
err=(oof-Y).squeeze(1).abs()
order=torch.argsort(err,descending=True)
print('Top-8 abs errors (true, pred, err):')
for i in order[:8]:
    print(f'  true={float(Y[i,0]):.3f} pred={float(oof[i,0]):.3f} err={float(err[i]):.3f}')
import numpy as np
yb=Y.squeeze(1).numpy(); eb=err.numpy()
for lo,hi in [(0,0.5),(0.5,0.9),(0.9,1.3),(1.3,3)]:
    msk=(yb>=lo)&(yb<hi); print(f'  mass[{lo},{hi}): n={int(msk.sum()):3d} meanerr={eb[msk].mean():.3f} rms={np.sqrt((eb[msk]**2).mean()):.3f}')
print('overall rel', float(torch.sqrt((err**2).sum())/torch.sqrt((Y.squeeze(1)**2).sum())))
"

output

Top-8 abs errors (true, pred, err):
  true=1.640 pred=0.787 err=0.852
  true=1.634 pred=0.875 err=0.759
  true=1.366 pred=0.719 err=0.647
  true=1.114 pred=0.507 err=0.607
  true=2.072 pred=1.497 err=0.575
  true=1.584 pred=1.015 err=0.569
  true=1.180 pred=0.619 err=0.561
  true=1.072 pred=0.572 err=0.500
  mass[0,0.5): n= 41 meanerr=0.073 rms=0.092
  mass[0.5,0.9): n= 56 meanerr=0.177 rms=0.200
  mass[0.9,1.3): n= 20 meanerr=0.297 rms=0.340
  mass[1.3,3): n= 15 meanerr=0.458 rms=0.496
overall rel 0.3009372651576996

Clear pattern: the model systematically UNDERPREDICTS heavy brackets. The heavy brackets (mass >1.3) have mean error 0.458, and they're all underpredicted (pred << true). This is classic regression-to-the-mean due to: (a) few heavy samples (15 out of 132), (b) MSE in standardized space treats all equally but heavy ones are rare and extreme. This underprediction dominates both rel L2 (numerator sum of squared errors is dominated by these big errors) and max-abs (0.85). So fixing heavy-bracket underprediction is the single biggest lever. Strategies: 1. **Sample weighting**: weight loss by mass (or mass^2, or 1/freq) so heavy brackets contribute more. This directly combats underprediction. Let me weight ∝ mass^p. 2. **Log-space targets**: predicting log(mass) would make the relative errors more uniform and reduce the dominance of scale, but rel L2 is in linear space. Predicting log and exponentiating tends to reduce underprediction of large values because the loss on log puts more relative weight on large values? Actually log compresses large values, so MSE in log space weights relative error equally → heavy brackets get effectively more weight than linear MSE (where a 10% error on 2.0 = 0.2 abs vs 10% on 0.5 = 0.05). Hmm, linear MSE already weights heavy errors more in absolute terms. The underprediction is more about data scarcity + regularization pulling toward mean. Let me just try sample weighting ∝ mass^1 and mass^2, and also try combining with the density/heavy oversampling. 3. **Oversample heavy brackets** in minibatches so the model sees them more. Let me add sample weighting to train_core. Weight w_i ∝ (mass_i)^p normalized. For the loss, weighted MSE = mean(w_i * (pred_i-y_i)^2). Let me implement with p configurable. Let me add 'weight_pow' to cfg. In train_core, precompute weights from Ytr (physical mass). Then weighted loss. Let me also consider that the rel L2 metric itself: minimizing sum e_i^2 is plain MSE. But the model underfits heavy due to regularization/scarcity. Weighting will trade light-sample accuracy for heavy-sample accuracy. Since heavy errors are ~0.45 and light errors ~0.07, shifting weight to heavy should reduce the big errors a lot while slightly increasing small ones → net rel L2 improvement. Let me test. Let me add weighting and run a sweep: - weight_pow in {0, 1, 2} - also try log-target mode Let me implement both weighting and an optional log-target. For log-target: transform y -> log(y), standardize, train MSE, then at inference exp back. I'd need label_mean/label_std to represent log-space standardization AND the grader de-standardizes linearly then... wait. The grader does pred*label_std+label_mean to get physical mass. If I train in log space, the model outputs standardized-log, de-standardize gives log-mass, but grader expects physical mass = pred*std+mean. So log-space is INCOMPATIBLE with the fixed inference pipeline (which only does linear de-standardization). Unless I bake exp into... no, I can't change inference. So I MUST output in linear physical space via linear de-standardization. So log-target is out (the grader applies pred*label_std+label_mean and compares to physical mass). Wait, let me double check the inference contract. "label_mean, label_std - length-1 tensors used to standardize the target during training and de-standardize the model output at inference. The model's output is mapped back to physical units using these." So physical = output*label_std + label_mean. Strictly linear. So I cannot use log targets. Confirmed: linear target standardization only. Good to know. So sample weighting is the main lever (plus better features/regularization). Let me implement weighting. Let me add to train_core a 'wpow' param. Let me also reconsider whether predicting mass well requires the model to estimate the physical scale. Since the underprediction is systematic, maybe the model CAN'T distinguish heavy from medium well because the signal (scale from interface) is subt …[truncated]

The model systematically **underpredicts heavy brackets** (mass >1.3 has mean error 0.46, all underpredicted) , a regression-to-mean effect from scarcity of heavy samples. This dominates both rel-L2 and max-error. Note: log-target is impossible here since the grader only de-standardizes linearly (`output*label_std + label_mean`). So the main lever is **mass-weighted loss / oversampling**. Let me add sample weighting to the trainer.

str_replace_based_edit_tool

command: str_replace
path: /app/scratch/trainlib.py
old_str:     yn_all = (Ytr - lmean) / lstd
    snaps = {}
    step = 0
    snapset = set(snapshot_epochs)
new_str:     yn_all = (Ytr - lmean) / lstd
    wpow = cfg_over.get("wpow", 0.0)
    wts = (Ytr.squeeze(1) ** wpow)
    wts = wts / wts.mean()                       # normalized sample weights [N]
    snaps = {}
    step = 0
    snapset = set(snapshot_epochs)

output

The file /app/scratch/trainlib.py has been edited successfully.

str_replace_based_edit_tool

command: str_replace
path: /app/scratch/trainlib.py
old_str:             pred = model(xbn)
            yb = yn_all[idx]
            if loss_kind == "mse":
                loss = F.mse_loss(pred, yb)
            elif loss_kind == "huber":
                loss = F.smooth_l1_loss(pred, yb, beta=huber_beta)
            elif loss_kind == "mse+l1":
                loss = F.mse_loss(pred, yb) + 0.3 * F.l1_loss(pred, yb)
new_str:             pred = model(xbn)
            yb = yn_all[idx]
            w = wts[idx].unsqueeze(1)
            if loss_kind == "mse":
                loss = (w * (pred - yb) ** 2).mean()
            elif loss_kind == "huber":
                loss = (w * F.smooth_l1_loss(pred, yb, beta=huber_beta, reduction="none")).mean()
            elif loss_kind == "mse+l1":
                loss = (w * ((pred - yb) ** 2 + 0.3 * (pred - yb).abs())).mean()

output

The file /app/scratch/trainlib.py has been edited successfully.

Now let me sweep wpow ∈ {0, 1, 2} and also try stronger regularization. Let me run a sweep across a few configs via CV and report best-epoch per config. To manage runtime, I'll use 6 folds, epochs=300, snapshots every 50. Let me write a sweep script.

Now let me sweep the mass-weighting power and regularization strength via 6-fold CV.

bash

cd /app/scratch && cat > sweep.py << 'EOF'
import torch, trainlib as T, time, sys
trX,trY=T.load_split('train'); vaX,vaY=T.load_split('val')
X=torch.cat([trX,vaX],0); Y=torch.cat([trY,vaY],0)
snaps=[100,150,200,250,300,350,400]
configs = {
 'w0_base':      dict(wpow=0.0, wd=1e-4, aug=dict(flip=True,jitter=0.01,resample=0.15)),
 'w1_base':      dict(wpow=1.0, wd=1e-4, aug=dict(flip=True,jitter=0.01,resample=0.15)),
 'w2_base':      dict(wpow=2.0, wd=1e-4, aug=dict(flip=True,jitter=0.01,resample=0.15)),
 'w1_reg':       dict(wpow=1.0, wd=1e-3, aug=dict(flip=True,jitter=0.02,resample=0.30)),
 'w2_reg':       dict(wpow=2.0, wd=1e-3, aug=dict(flip=True,jitter=0.02,resample=0.30)),
}
base=dict(seed=0, epochs=400, bs=16, lr=1.5e-3, warmup=20, loss='mse', isotropic=True)
for name,over in configs.items():
    cfg=dict(base); cfg.update(over)
    t=time.time(); res=T.run_cv(cfg,(X,Y),n_folds=6,snapshot_epochs=snaps,seed=0)
    best=min(((res[(e,'raw')][0],e,'raw') for e in snaps))
    print(f'[{name}] ({time.time()-t:.0f}s) best raw rel={best[0]:.4f} @ep{best[1]}')
    for e in snaps:
        r=res[(e,'raw')]; print(f'     ep{e:4d} raw rel={r[0]:.4f} mae={r[1]:.4f} mx={r[2]:.4f}')
    sys.stdout.flush()
EOF
python sweep.py

output

[stderr]
[w0_base] (125s) best raw rel=0.2811 @ep400
     ep 100 raw rel=0.3589 mae=0.2271 mx=1.0161
     ep 150 raw rel=0.3500 mae=0.2232 mx=0.8778
     ep 200 raw rel=0.3470 mae=0.2174 mx=1.0023
     ep 250 raw rel=0.3071 mae=0.2043 mx=0.6935
     ep 300 raw rel=0.2903 mae=0.1947 mx=0.7389
     ep 350 raw rel=0.2846 mae=0.1914 mx=0.6849
     ep 400 raw rel=0.2811 mae=0.1885 mx=0.6949
[w1_base] (118s) best raw rel=0.2586 @ep400
     ep 100 raw rel=0.3553 mae=0.2431 mx=0.8267
     ep 150 raw rel=0.3229 mae=0.2058 mx=0.8235
     ep 200 raw rel=0.3012 mae=0.1954 mx=0.7060
     ep 250 raw rel=0.2778 mae=0.1786 mx=0.7370
     ep 300 raw rel=0.2740 mae=0.1806 mx=0.7131
     ep 350 raw rel=0.2646 mae=0.1754 mx=0.7038
     ep 400 raw rel=0.2586 mae=0.1718 mx=0.6768Shell command timed out.
Tool interaction exceeded 300 seconds.

Good progress. wpow=1.0 helps (rel 0.2586 vs 0.2811 at ep400, and better max 0.68 and mae 0.172). Both improve monotonically up to ep400, so more epochs may help further. Let me restart the tool and run fewer configs per call (to stay under 300s), and extend epochs. Observations: - w1 (mass weighting) clearly better than w0 on all three metrics. - Still improving at ep400 → train longer (maybe 600-800). - Need to test w2 and regularized variants, and longer epochs. Let me restart and run in background with nohup to avoid timeout, writing logs to files. That's the robust approach for long runs.

Mass-weighting (wpow=1) clearly helps: rel-L2 0.259 vs 0.281, with better MAE and max-error. Both still improving at ep400. Let me restart the tool and run longer experiments in the background to avoid timeouts.

bash

restart: true

output

<system>tool has been restarted.</system>

bash

cd /app/scratch && cat > sweep2.py << 'EOF'
import torch, trainlib as T, time, sys
trX,trY=T.load_split('train'); vaX,vaY=T.load_split('val')
X=torch.cat([trX,vaX],0); Y=torch.cat([trY,vaY],0)
snaps=[300,400,500,600,700,800]
configs = {
 'w1_long':  dict(wpow=1.0, wd=1e-4, aug=dict(flip=True,jitter=0.01,resample=0.15)),
 'w2_long':  dict(wpow=2.0, wd=1e-4, aug=dict(flip=True,jitter=0.01,resample=0.15)),
 'w1_reg':   dict(wpow=1.0, wd=1e-3, aug=dict(flip=True,jitter=0.02,resample=0.30)),
 'w15_reg':  dict(wpow=1.5, wd=5e-4, aug=dict(flip=True,jitter=0.015,resample=0.25)),
}
base=dict(seed=0, epochs=800, bs=16, lr=1.5e-3, warmup=20, loss='mse', isotropic=True)
for name,over in configs.items():
    cfg=dict(base); cfg.update(over)
    t=time.time(); res=T.run_cv(cfg,(X,Y),n_folds=6,snapshot_epochs=snaps,seed=0)
    best=min(((res[(e,'raw')][0],e) for e in snaps))
    print(f'[{name}] ({time.time()-t:.0f}s) BEST raw rel={best[0]:.4f} @ep{best[1]}',flush=True)
    for e in snaps:
        r=res[(e,'raw')]; print(f'     ep{e:4d} rel={r[0]:.4f} mae={r[1]:.4f} mx={r[2]:.4f}',flush=True)
EOF
nohup python sweep2.py > sweep2.log 2>&1 &
echo "launched pid $!"

output

launched pid 366

While that runs, let me think about other improvements. Let me wait and monitor. Meanwhile, let me reconsider the approach. Actually, let me reconsider whether the eval of EMA at the fold level is even needed. I'll skip EMA going forward (it's worse). run_cv computes both but that roughly doubles eval cost (recalibration). Minor. Let me let it run. Let me think about other levers while waiting: 1. **Test-time augmentation (TTA)**: At inference, I can't change the grader's forward. The grader feeds the raw [B,2048,3] once. So no TTA at grading time. BUT , could I make the model robust by training with augmentation so a single forward is good. TTA not available since grader does single forward. Unless... the grader just calls model(x). I can't wrap. So no TTA. 2. **Ensemble into one model**: Can't , single state_dict. But I could train with multiple seeds and average the WEIGHTS? Averaging weights of independently-initialized networks generally doesn't work (permutation symmetry). Only works for same-init fine-tunes (SWA). So no naive ensemble. However, I could do a "snapshot ensemble" via SWA: average weights from late epochs of a SINGLE run (same init), which lie in the same basin → valid. SWA often improves generalization. But earlier EMA (which is similar to SWA) hurt because it regressed heavy brackets. Hmm, but that was EMA over a long window including pre-convergence. SWA over the last few epochs (after convergence, with cyclic LR) might help. Given time constraints, I'll consider it if basic approach plateaus. 3. **BatchNorm at inference**: The grader calls model.eval() presumably → uses running stats. I must ensure running stats are well-estimated for the FINAL model. When I train the final model on all data, the running stats come from augmented batches. For best inference, I should recalibrate BN on clean (un-augmented) normalized training data at the end. I'll do that for the final model. Actually important subtlety: does the grader call eval()? If it does NOT, and calls model(x) in train mode with a batch, BN uses batch stats → different. Standard practice is eval(). The starter code uses model.eval() for val. I'll assume eval(). To be safe, I could make the model robust to both by having good running stats (eval) , can't control train-mode behavior. I'll assume eval. 4. **The normalization (isotropic vs per-axis)**: I used isotropic. Let me test per-axis too; it might help or hurt. Minor; test later. 5. **Loss for worst-case**: The max error is ~0.68 now. Mass weighting helps. Could also help by clamping predictions to the training range? No, can't post-process in grader. Let me also reconsider the fundamental ceiling. Let me, in parallel, test a richer classical model (voxel + PCA features + ridge with more features) to see if ~0.25 is near the ceiling or if there's more signal. Actually, let me try a small MLP on voxel features in torch to estimate the ceiling, using CV. But I should conserve compute while sweep2 runs (3 CPUs, 1 GPU , the sweep uses GPU). Let me wait for sweep2 and think. Let me check on sweep2 progress periodically. Each config ~ (800 epochs × 6 folds). Earlier 400ep×6folds×(with ema recal) took ~120s. 800 ep → ~240s per config. 4 configs → ~16 min. Let me wait and check. Let me monitor.

While the sweep runs, let me note key constraints I've confirmed: the grader does a single forward (no test-time augmentation possible), only a single `state_dict` can be saved (no naive ensemble), and targets must be linear (no log-space). Let me monitor progress.

bash

cd /app/scratch && sleep 180 && cat sweep2.log

output

<system>Tool ran without output or errors</system>

bash

cd /app/scratch && sleep 60 && cat sweep2.log; echo "--- running? ---"; pgrep -f sweep2.py && echo yes || echo no

output

[w1_long] (194s) BEST raw rel=0.2257 @ep600
     ep 300 rel=0.2746 mae=0.1791 mx=0.7363
     ep 400 rel=0.2847 mae=0.1872 mx=0.6473
     ep 500 rel=0.2621 mae=0.1758 mx=0.5551
     ep 600 rel=0.2257 mae=0.1434 mx=0.6546
     ep 700 rel=0.2398 mae=0.1595 mx=0.6282
     ep 800 rel=0.2341 mae=0.1563 mx=0.5819
--- running? ---
366
yes

w1_long at ep600 reaches rel 0.2257, mae 0.143, mx 0.655. That's better than ridge baseline (0.206 val but that was single split; CV ridge would be ~0.25 probably). This is now solidly good. The best epoch is around 600. Note there's noise across epochs (400 worse than 300). Let me wait for the other configs. Let me let it finish.

w1_long reaches rel-L2 0.226 at ep600. Let me wait for the remaining configs.

bash

cd /app/scratch && sleep 240 && cat sweep2.log; echo "--- running? ---"; pgrep -f sweep2.py && echo yes || echo no

output

[w1_long] (194s) BEST raw rel=0.2257 @ep600
     ep 300 rel=0.2746 mae=0.1791 mx=0.7363
     ep 400 rel=0.2847 mae=0.1872 mx=0.6473
     ep 500 rel=0.2621 mae=0.1758 mx=0.5551
     ep 600 rel=0.2257 mae=0.1434 mx=0.6546
     ep 700 rel=0.2398 mae=0.1595 mx=0.6282
     ep 800 rel=0.2341 mae=0.1563 mx=0.5819
[w2_long] (214s) BEST raw rel=0.2154 @ep800
     ep 300 rel=0.2659 mae=0.1786 mx=0.6566
     ep 400 rel=0.2774 mae=0.1815 mx=0.6753
     ep 500 rel=0.2389 mae=0.1624 mx=0.6113
     ep 600 rel=0.2228 mae=0.1439 mx=0.6475
     ep 700 rel=0.2212 mae=0.1487 mx=0.6311
     ep 800 rel=0.2154 mae=0.1421 mx=0.6273
--- running? ---
366
yes

w2_long reaches rel 0.2154 at ep800, still improving. wpow=2 is a bit better than wpow=1. Let me wait for the reg configs. These suggest longer training + heavier weighting helps. Let me continue waiting. Note the metrics are noisy epoch-to-epoch. I should average over multiple seeds to get robust estimates and reduce variance. Also the final model benefits from an ensemble-like stability. Let me plan: after identifying the best config family, run multiple seeds and average predictions in CV to confirm, then train final. Let me wait for the rest.

wpow=2 edges ahead (rel 0.215 at ep800, still improving). Let me wait for the regularized configs to finish.

bash

cd /app/scratch && sleep 240 && tail -n +8 sweep2.log; echo "--- running? ---"; pgrep -f sweep2.py && echo yes || echo no

output

[w2_long] (214s) BEST raw rel=0.2154 @ep800
     ep 300 rel=0.2659 mae=0.1786 mx=0.6566
     ep 400 rel=0.2774 mae=0.1815 mx=0.6753
     ep 500 rel=0.2389 mae=0.1624 mx=0.6113
     ep 600 rel=0.2228 mae=0.1439 mx=0.6475
     ep 700 rel=0.2212 mae=0.1487 mx=0.6311
     ep 800 rel=0.2154 mae=0.1421 mx=0.6273
[w1_reg] (210s) BEST raw rel=0.3781 @ep300
     ep 300 rel=0.3781 mae=0.2482 mx=0.8982
     ep 400 rel=0.3978 mae=0.2550 mx=0.9604
     ep 500 rel=0.4048 mae=0.2675 mx=0.9276
     ep 600 rel=0.3830 mae=0.2575 mx=0.8705
     ep 700 rel=0.3900 mae=0.2653 mx=0.9122
     ep 800 rel=0.3867 mae=0.2644 mx=0.8924
--- running? ---
366
yes

Interesting! The "w1_reg" config (wd=1e-3, jitter=0.02, resample=0.30) is MUCH WORSE (rel 0.378). So strong regularization (high weight decay + heavy augmentation) HURTS. The model needs to fit the data; too much aug/wd prevents learning the scale signal. So the lighter augmentation (jitter 0.01, resample 0.15) with low wd (1e-4) and longer training is better. This makes sense: the signal for mass (scale inference from fine geometric features like hole sizes) is subtle, and heavy jitter/resampling destroys it. So I should use LIGHT augmentation. Let me reconsider: maybe even lighter augmentation is better? Or is flip the main useful one? Let me test: - flip only (no jitter, no resample) - flip + tiny jitter 0.005 - flip + resample 0.1 And confirm wpow=2, wd=1e-4, long training. Let me wait for w15_reg to finish (it's moderate reg), then launch a focused augmentation sweep. Actually w15_reg is running now. Let me wait a bit then launch the next sweep. Let me check if it's done.

Important finding: heavy regularization (w1_reg: wd=1e-3, jitter=0.02, resample=0.30) is much worse (rel 0.378). The mass signal is subtle, so aggressive augmentation destroys it. Light augmentation + low weight decay + long training wins. Let me wait for the last config, then sweep lighter augmentation.

bash

cd /app/scratch && sleep 150 && tail -n 8 sweep2.log; echo "--- running? ---"; pgrep -f sweep2.py && echo yes || echo no

output

ep 800 rel=0.3867 mae=0.2644 mx=0.8924
[w15_reg] (193s) BEST raw rel=0.2886 @ep700
     ep 300 rel=0.3035 mae=0.2001 mx=0.7193
     ep 400 rel=0.3296 mae=0.2087 mx=0.8700
     ep 500 rel=0.3165 mae=0.2126 mx=0.6112
     ep 600 rel=0.2930 mae=0.1992 mx=0.6190
     ep 700 rel=0.2886 mae=0.1971 mx=0.5953
     ep 800 rel=0.2986 mae=0.2041 mx=0.5912
--- running? ---
no

Confirmed: moderate reg (w15_reg) also worse (0.289) than light aug (w2_long 0.215). So light augmentation is key. Best so far: w2_long (wpow=2, wd=1e-4, flip+jitter0.01+resample0.15), rel 0.215 @ep800, still improving. Now let me: 1. Test even lighter augmentation and longer training with wpow=2. 2. Test wpow=3. 3. Test no-resample / no-jitter variants. Let me design sweep3 with wpow=2 fixed, vary augmentation (lighter), and push epochs to 1000-1200. Also test wpow 2 vs 3. To keep under time, I'll run in background. Configs: - a: flip only, wpow2 - b: flip+jitter0.005, wpow2 - c: flip+jitter0.01+resample0.10, wpow2 - d: flip+jitter0.01, wpow2 (no resample) - e: flip+jitter0.01+resample0.15, wpow3 epochs=1200, snapshots [600,800,1000,1200]. 5 configs. Each ~ (1200/800)*210 ≈ 315s → 5 configs ~26min. That's long. Let me reduce to 4 configs and epochs=1000, snapshots [500,700,900,1000]. And reduce folds? Keep 6 for stability. ~260s each ×4 = ~17min. OK. Let me also disable the EMA eval in run_cv to save time , actually EMA recalibration adds ~30% eval time but eval is infrequent (only at snapshots). Minor. Keep. Actually, let me speed up by removing EMA computation from run_cv since we've decided EMA hurts. Let me add a flag. I'll modify train_core to skip ema eval when cfg 'skip_ema' True. Simpler: just leave it; it's only at snapshots. Let me launch sweep3.

Confirmed: light augmentation is essential. Best config: wpow=2, wd=1e-4, flip+jitter(0.01)+resample(0.15), still improving at ep800. Let me sweep even lighter augmentation variants and higher wpow with longer training.

bash

cd /app/scratch && cat > sweep3.py << 'EOF'
import torch, trainlib as T, time
trX,trY=T.load_split('train'); vaX,vaY=T.load_split('val')
X=torch.cat([trX,vaX],0); Y=torch.cat([trY,vaY],0)
snaps=[600,800,1000,1200]
configs = {
 'fliponly_w2':   dict(wpow=2.0, aug=dict(flip=True)),
 'flip_j005_w2':  dict(wpow=2.0, aug=dict(flip=True,jitter=0.005)),
 'flip_j01_w2':   dict(wpow=2.0, aug=dict(flip=True,jitter=0.01)),
 'light_w3':      dict(wpow=3.0, aug=dict(flip=True,jitter=0.01,resample=0.10)),
}
base=dict(seed=0, epochs=1200, bs=16, lr=1.5e-3, wd=1e-4, warmup=20, loss='mse', isotropic=True)
for name,over in configs.items():
    cfg=dict(base); cfg.update(over)
    t=time.time(); res=T.run_cv(cfg,(X,Y),n_folds=6,snapshot_epochs=snaps,seed=0)
    best=min(((res[(e,'raw')][0],e) for e in snaps))
    print(f'[{name}] ({time.time()-t:.0f}s) BEST rel={best[0]:.4f} @ep{best[1]}',flush=True)
    for e in snaps:
        r=res[(e,'raw')]; print(f'     ep{e:4d} rel={r[0]:.4f} mae={r[1]:.4f} mx={r[2]:.4f}',flush=True)
EOF
nohup python sweep3.py > sweep3.log 2>&1 &
echo "launched $!"

output

launched 425

While this runs (~17-25 min), let me think about the final training strategy and set up the final training script. Also let me think about multi-seed averaging for robustness. Key decisions for the final model: 1. Config: wpow ~2, wd=1e-4, light aug, lr 1.5e-3, ~800-1200 epochs. Will confirm from sweep3. 2. Training data: all labeled data (train+val+public_test = 148). More data → better generalization to hidden set. But I lose a monitor. I'll use the CV-chosen epoch budget. Since metrics are noisy per-epoch, I'll train to a fixed epoch and use BN recalibration at the end. Actually, important: there's epoch-to-epoch noise (e.g., ep400 worse than ep300). A single final checkpoint at a fixed epoch might land on a bad epoch. To reduce this variance, I should use weight averaging over the last K epochs (SWA) OR average predictions across multiple seeds. Since I can only save ONE model, SWA (averaging weights from late epochs of one run, same basin) is valid and reduces noise. Let me test SWA in CV: average weights of snapshots at, say, ep {900,1000,1100,1200} with constant low LR at the end, then recalibrate BN. Alternatively, I train multiple seeds and pick... no, can't ensemble. SWA is the way to get ensemble-like stability in one model. Let me implement an SWA variant: after the cosine schedule brings LR down, average the weights over the final few epochs. Actually simpler: collect weight snapshots at the last few snapshot epochs and average them, then BN-recalibrate. Let me add an SWA evaluation to the CV to see if it beats the best single epoch and reduces variance. Let me design: in train_core, also collect weight snapshots (state_dict copies) at snapshot epochs. Then I can form SWA averages post-hoc. But that increases memory (92k params × few snapshots, trivial). Let me add a 'collect_weights' option returning state_dicts at snapshots. Then in a CV variant, I average the last K state_dicts, recalibrate BN on train, and predict. Actually, the cleanest: implement SWA inside train_core , maintain a running average of weights during the last `swa_frac` fraction of epochs (e.g., last 30%), updated every epoch, with the cosine LR optionally flattened to a small constant during SWA. Then evaluate the SWA model (BN-recalibrated). This is standard SWA. Let me add SWA to train_core: - swa_start_frac (e.g., 0.6): after this fraction of epochs, start accumulating weight average (once per epoch). - Optionally set LR constant (swa_lr) during SWA phase, or keep cosine. Keep cosine for simplicity first; test. Let me add SWA and expose SWA predictions at the final epoch. Let me wait for sweep3 partial results first, but I can code SWA now. Let me modify trainlib to support SWA. I'll add an SWA accumulator and, at snapshot epochs within the SWA window, also produce 'swa' predictions. Actually, to keep it simpler and avoid disturbing the running experiment (which imports trainlib), I should NOT break trainlib while sweep3 runs , sweep3 already imported it into its process, so edits won't affect the running process. Safe to edit. But let me be careful to keep API backward compatible. Let me add SWA support.

The sweep will take ~20 min. While it runs, let me add **SWA (Stochastic Weight Averaging)** to the trainer , averaging weights over late epochs in the same loss basin reduces the epoch-to-epoch noise I observed, and it's valid to save as a single checkpoint. This should improve stability and worst-case error.

str_replace_based_edit_tool

command: str_replace
path: /app/scratch/trainlib.py
old_str:     yn_all = (Ytr - lmean) / lstd
    wpow = cfg_over.get("wpow", 0.0)
new_str:     swa_start = cfg_over.get("swa_start", None)   # epoch to begin SWA accumulation
    swa_lr = cfg_over.get("swa_lr", None)
    swa_state = None
    swa_count = 0

    yn_all = (Ytr - lmean) / lstd
    wpow = cfg_over.get("wpow", 0.0)

output

The file /app/scratch/trainlib.py has been edited successfully.

str_replace_based_edit_tool

command: str_replace
path: /app/scratch/trainlib.py
old_str:     def lr_at(st):
        if st < warm * steps_per:
            return lr * (st + 1) / (warm * steps_per)
        p = (st - warm * steps_per) / max(1, total - warm * steps_per)
        return lr * 0.5 * (1 + math.cos(math.pi * p))
new_str:     def lr_at(st):
        ep_now = st // steps_per
        if swa_start is not None and swa_lr is not None and ep_now >= swa_start:
            return swa_lr
        if st < warm * steps_per:
            return lr * (st + 1) / (warm * steps_per)
        denom_end = (swa_start * steps_per) if swa_start is not None else total
        p = (st - warm * steps_per) / max(1, denom_end - warm * steps_per)
        p = min(p, 1.0)
        return lr * 0.5 * (1 + math.cos(math.pi * p))

output

The file /app/scratch/trainlib.py has been edited successfully.

Now add the SWA accumulation at the end of each epoch, and produce swa predictions at snapshots. Let me add after the inner loop (before snapshot block) the SWA update, and in the snapshot block add swa predictions.

str_replace_based_edit_tool

command: str_replace
path: /app/scratch/trainlib.py
old_str:             opt.step(); ema.update(model); step += 1
        if (ep + 1) in snapset:
new_str:             opt.step(); ema.update(model); step += 1
        # SWA: accumulate running average of weights once per epoch
        if swa_start is not None and (ep + 1) >= swa_start:
            sd = model.state_dict()
            if swa_state is None:
                swa_state = {k: v.detach().clone().float() for k, v in sd.items()}
                swa_count = 1
            else:
                swa_count += 1
                for k, v in sd.items():
                    if v.dtype.is_floating_point:
                        swa_state[k].mul_(1 - 1.0 / swa_count).add_(v.detach().float(), alpha=1.0 / swa_count)
                    else:
                        swa_state[k] = v.detach().clone().float()
        if (ep + 1) in snapset:

output

The file /app/scratch/trainlib.py has been edited successfully.

str_replace_based_edit_tool

command: str_replace
path: /app/scratch/trainlib.py
old_str:                 pe = pe * lstd + lmean
            snaps[ep + 1] = {"raw": pr.cpu(), "ema": pe.cpu()}
    return snaps, (fmean.cpu(), fstd.cpu(), lmean.cpu(), lstd.cpu())
new_str:                 pe = pe * lstd + lmean
                entry = {"raw": pr.cpu(), "ema": pe.cpu()}
                if swa_state is not None:
                    swa_model = build_model(cfg).to(DEV)
                    swa_model.load_state_dict({k: v.to(DEV) for k, v in swa_state.items()})
                    bn_recalibrate(swa_model, calib_n)
                    ps = torch.cat([swa_model(xten[i:i+64]) for i in range(0, xten.shape[0], 64)], 0)
                    entry["swa"] = (ps * lstd + lmean).cpu()
            snaps[ep + 1] = entry
    return snaps, (fmean.cpu(), fstd.cpu(), lmean.cpu(), lstd.cpu()), swa_state

output

The file /app/scratch/trainlib.py has been edited successfully.

I changed the return signature of train_core to return 3 values. But run_cv calls `snaps, _ = train_core(...)`. Let me fix run_cv to unpack 3 and also handle swa in OOF. Let me update run_cv.

str_replace_based_edit_tool

command: str_replace
path: /app/scratch/trainlib.py
old_str:     oof = {e: {"raw": torch.zeros(N, 1), "ema": torch.zeros(N, 1)} for e in snapshot_epochs}
    for k in range(n_folds):
        te_idx = folds[k]
        tr_mask = torch.ones(N, dtype=torch.bool); tr_mask[te_idx] = False
        Xtr, Ytr = X[tr_mask], Y[tr_mask]
        Xte = X[te_idx]
        snaps, _ = train_core(cfg_over, Xtr, Ytr, Xte, snapshot_epochs)
        for e in snapshot_epochs:
            oof[e]["raw"][te_idx] = snaps[e]["raw"]
            oof[e]["ema"][te_idx] = snaps[e]["ema"]
    results = {}
    for e in snapshot_epochs:
        for w in ["raw", "ema"]:
            results[(e, w)] = metrics_vec(oof[e][w], Y)
    return results
new_str:     whichs = cfg_over.get("eval_which", ["raw"])
    oof = {e: {w: torch.zeros(N, 1) for w in whichs} for e in snapshot_epochs}
    for k in range(n_folds):
        te_idx = folds[k]
        tr_mask = torch.ones(N, dtype=torch.bool); tr_mask[te_idx] = False
        Xtr, Ytr = X[tr_mask], Y[tr_mask]
        Xte = X[te_idx]
        snaps, _, _ = train_core(cfg_over, Xtr, Ytr, Xte, snapshot_epochs)
        for e in snapshot_epochs:
            for w in whichs:
                if w in snaps[e]:
                    oof[e][w][te_idx] = snaps[e][w]
    results = {}
    for e in snapshot_epochs:
        for w in whichs:
            results[(e, w)] = metrics_vec(oof[e][w], Y)
    return results

output

The file /app/scratch/trainlib.py has been edited successfully.

Wait , sweep3 is currently running and imported the OLD trainlib (before these edits). Since Python imports are loaded into memory, the running process uses the old code, so my edits won't break it. Good. But let me verify sweep3 is still running and not affected. Also, I must make sure my edits are syntactically correct so future runs work. Let me check that the module still imports. Actually, the running sweep3 process already has the old module loaded; editing the file on disk doesn't affect it. Let me verify the new file parses.

Since sweep3 already loaded the old module into memory, my edits won't disrupt it. Let me verify the updated module parses correctly and check sweep3 progress.

bash

cd /app/scratch && python -c "import importlib,trainlib; importlib.reload(trainlib); print('trainlib OK')" && echo "--- sweep3 so far ---" && cat sweep3.log; pgrep -f sweep3.py >/dev/null && echo "[running]" || echo "[done]"

output

trainlib OK
--- sweep3 so far ---
[running]

sweep3 is still running (no output yet , first config takes ~300s for 1200 epochs). Let me wait. Meanwhile let me plan the final pipeline and write the final training script that uses SWA. Let me think about the final model approach more concretely: - Use wpow≈2, wd=1e-4, light aug (flip + jitter 0.01, maybe small resample), lr 1.5e-3. - Cosine decay until swa_start, then constant small swa_lr, accumulate SWA over last ~30-40% epochs. - Train on ALL 148 labeled samples (train+val+public_test). - Recalibrate BN on clean normalized full training data. - Save SWA weights + norm constants. But I need to validate SWA helps via CV first. Let me wait for sweep3 and then run an SWA-focused CV. Let me also reconsider: should the final model train on all 148 or hold out public_test? The hidden test set is separate. Using all labeled data maximizes training signal. The CV (on 132) gives me the epoch/config; I'll apply to all 148. I'm fairly confident more data helps. I'll train final on all 148. One more consideration: variance reduction. Even SWA from one run has seed variance. I could train the final SWA model and also verify its CV-estimated performance. Since I can only save one model, I'll pick the config+seed that looks most robust. Actually, a neat trick: I can run SWA over a LONG tail and multiple cosine cycles (cyclic SWA) to average more diverse points → more ensemble-like. But let me first confirm basic SWA helps. Let me wait for sweep3.

sweep3 is running (1200-epoch configs take ~5 min each). Let me wait for results.

bash

cd /app/scratch && sleep 290 && cat sweep3.log; pgrep -f sweep3.py >/dev/null && echo "[running]" || echo "[done]"

output

[fliponly_w2] (239s) BEST rel=0.1950 @ep1200
     ep 600 rel=0.2626 mae=0.1687 mx=0.7676
     ep 800 rel=0.2220 mae=0.1366 mx=0.8005
     ep1000 rel=0.1962 mae=0.1208 mx=0.6401
     ep1200 rel=0.1950 mae=0.1160 mx=0.6655
[running]

Big improvement! `fliponly_w2` (flip only, no jitter, no resample, wpow=2) reaches rel 0.195, mae 0.116, mx 0.666 at ep1200, still improving! So jitter and resample were HURTING. Flip-only augmentation + mass weighting + long training is best so far. This beats ridge (0.21). So the mass signal is in fine geometry that even small jitter (0.01) degrades. Flip (reflections) is the only safe augmentation. Let me push further: - Flip only, longer (2000 epochs), wpow 2. - Maybe wpow 2.5-3. - Maybe flip + tiny jitter 0.002. Let me wait for the rest of sweep3 (flip_j005, flip_j01, light_w3), then launch a flip-only long sweep with SWA. Let me wait.

Major finding: **flip-only augmentation** (no jitter, no resample) with wpow=2 reaches rel-L2 0.195, MAE 0.116 , and still improving at ep1200. Even tiny jitter was degrading the subtle mass signal. Let me wait for the remaining configs to confirm.

bash

cd /app/scratch && sleep 280 && cat sweep3.log; pgrep -f sweep3.py >/dev/null && echo "[running]" || echo "[done]"

output

[fliponly_w2] (239s) BEST rel=0.1950 @ep1200
     ep 600 rel=0.2626 mae=0.1687 mx=0.7676
     ep 800 rel=0.2220 mae=0.1366 mx=0.8005
     ep1000 rel=0.1962 mae=0.1208 mx=0.6401
     ep1200 rel=0.1950 mae=0.1160 mx=0.6655
[flip_j005_w2] (242s) BEST rel=0.2020 @ep1200
     ep 600 rel=0.2532 mae=0.1555 mx=0.7203
     ep 800 rel=0.2150 mae=0.1306 mx=0.7721
     ep1000 rel=0.2065 mae=0.1265 mx=0.7030
     ep1200 rel=0.2020 mae=0.1224 mx=0.6942
[running]

flip_j005 (jitter 0.005) gives 0.202, slightly worse than flip-only (0.195). Confirms jitter hurts slightly. Let me wait for flip_j01 and light_w3. Then I'll run a flip-only long sweep with wpow variations and SWA. Let me wait.

bash

cd /app/scratch && sleep 280 && tail -n 12 sweep3.log; pgrep -f sweep3.py >/dev/null && echo "[running]" || echo "[done]"

output

ep1000 rel=0.1962 mae=0.1208 mx=0.6401
     ep1200 rel=0.1950 mae=0.1160 mx=0.6655
[flip_j005_w2] (242s) BEST rel=0.2020 @ep1200
     ep 600 rel=0.2532 mae=0.1555 mx=0.7203
     ep 800 rel=0.2150 mae=0.1306 mx=0.7721
     ep1000 rel=0.2065 mae=0.1265 mx=0.7030
     ep1200 rel=0.2020 mae=0.1224 mx=0.6942
[flip_j01_w2] (231s) BEST rel=0.2113 @ep1200
     ep 600 rel=0.2544 mae=0.1627 mx=0.7577
     ep 800 rel=0.2300 mae=0.1506 mx=0.6723
     ep1000 rel=0.2169 mae=0.1397 mx=0.6405
     ep1200 rel=0.2113 mae=0.1331 mx=0.6409
[running]

Clear monotonic trend: more jitter = worse. - flip only: 0.195 - jitter 0.005: 0.202 - jitter 0.01: 0.211 So flip-only is best. Let me wait for light_w3 (wpow3 + resample0.10 + jitter0.01) , probably worse due to resample/jitter. Then launch the definitive sweep: flip-only, vary wpow {1.5, 2, 2.5, 3}, epochs 2000, with SWA over the tail, using snapshots to find the best. Let me kill waiting for light_w3; it's the last config and nearly done. Let me just wait ~a bit then launch the flip-only long sweep with SWA. Let me check light_w3 result.

The trend is clear and monotonic: flip-only (0.195) > jitter 0.005 (0.202) > jitter 0.01 (0.211). Flip-only is best, still improving at ep1200. Let me launch the definitive sweep: flip-only, varying wpow, extended to 2000 epochs, now with SWA over the tail. First let me confirm the last config finished.

bash

cd /app/scratch && sleep 60 && tail -n 6 sweep3.log; pgrep -f sweep3.py >/dev/null && echo "[running]" || echo "[done]"

output

ep1200 rel=0.2020 mae=0.1224 mx=0.6942
[flip_j01_w2] (231s) BEST rel=0.2113 @ep1200
     ep 600 rel=0.2544 mae=0.1627 mx=0.7577
     ep 800 rel=0.2300 mae=0.1506 mx=0.6723
     ep1000 rel=0.2169 mae=0.1397 mx=0.6405
     ep1200 rel=0.2113 mae=0.1331 mx=0.6409
[running]

bash

cd /app/scratch && sleep 120 && tail -n 6 sweep3.log; pgrep -f sweep3.py >/dev/null && echo "[running]" || echo "[done]"

output

ep1200 rel=0.2113 mae=0.1331 mx=0.6409
[light_w3] (276s) BEST rel=0.2225 @ep1000
     ep 600 rel=0.2613 mae=0.1687 mx=0.9144
     ep 800 rel=0.2374 mae=0.1545 mx=0.7386
     ep1000 rel=0.2225 mae=0.1447 mx=0.6864
     ep1200 rel=0.2235 mae=0.1431 mx=0.6484
[done]

light_w3 (with resample+jitter) is 0.222 , worse, confirming resample+jitter hurt. Now the definitive sweep: flip-only, wpow in {1.5, 2, 2.5, 3}, epochs=2000, with SWA starting at ep~1400 (constant swa_lr), snapshots to evaluate raw vs swa. Let me also evaluate 'swa' in eval_which. Since flip-only is cheap (no resample/jitter overhead), each epoch is fast. Let me run epochs=2000, 6 folds. Snapshots [1200,1500,1800,2000]. SWA start at 1200 with swa_lr small (e.g., 3e-4 or 1e-4). Let me use swa_lr = 2e-4, swa_start=1200. eval_which = ['raw','swa']. 4 wpow values × ~ (2000 ep × 6 folds). Each fold 2000 ep flip-only. Earlier 1200ep flip-only fold→ 239s/6≈40s/fold for 1200ep → ~66s/fold for 2000ep → ×6 = ~400s per config ×4 = ~27min. That's long but OK in background. Let me also reduce to 3 wpow values {2, 2.5, 3} to save time, plus include SWA. Actually let me keep {1.5,2,2.5,3} but run in background and check periodically. Wait, I want to be efficient with my remaining budget. Let me do wpow {2, 2.5, 3} with SWA, epochs 2000. 3 configs ~20min. Let me launch.

light_w3 confirms resample+jitter hurt. Flip-only is decisively best. Now the definitive sweep: flip-only with wpow ∈ {2, 2.5, 3}, extended to 2000 epochs, with SWA over the tail (comparing raw vs SWA predictions).

bash

cd /app/scratch && cat > sweep4.py << 'EOF'
import torch, trainlib as T, time
trX,trY=T.load_split('train'); vaX,vaY=T.load_split('val')
X=torch.cat([trX,vaX],0); Y=torch.cat([trY,vaY],0)
snaps=[1200,1500,1800,2000]
base=dict(seed=0, epochs=2000, bs=16, lr=1.5e-3, wd=1e-4, warmup=20, loss='mse',
          isotropic=True, aug=dict(flip=True), swa_start=1200, swa_lr=2e-4,
          eval_which=['raw','swa'])
for wp in [2.0, 2.5, 3.0]:
    cfg=dict(base); cfg['wpow']=wp
    t=time.time(); res=T.run_cv(cfg,(X,Y),n_folds=6,snapshot_epochs=snaps,seed=0)
    print(f'[wpow={wp}] ({time.time()-t:.0f}s)',flush=True)
    for e in snaps:
        for w in ['raw','swa']:
            if (e,w) in res:
                r=res[(e,w)]; print(f'     ep{e:4d} {w}: rel={r[0]:.4f} mae={r[1]:.4f} mx={r[2]:.4f}',flush=True)
EOF
nohup python sweep4.py > sweep4.log 2>&1 &
echo "launched $!"

output

launched 557

While sweep4 runs (~20min), let me write the final training script so it's ready. It will: 1. Load all three splits, pool them. 2. Use the best config (to be confirmed: flip-only, wpow TBD, epochs TBD, SWA). 3. Train on all pooled data with SWA. 4. Recalibrate BN on clean normalized pooled data. 5. Save checkpoint in the required format to /app/outputs/model.pt. But I also want robustness: the final model is trained once on all data; seed variance matters. SWA reduces it. I could also average across seeds via... no, single model. Actually, here's a thought for even better single-model robustness: train with MULTIPLE cosine cycles and SWA across cycles (each cycle re-warms LR then decays, and we snapshot at each cycle's end) , this gives more diverse weights in the average, closer to an ensemble, while staying one model. But SWA across cycles can drift basins. Standard SWA with constant low LR in the tail is simpler and proven. Let me stick with tail-SWA. Let me also reconsider: should the final model be the SWA model or best raw? I'll decide from sweep4 (whether SWA beats raw). Let me write the final script to support both and a flag. Let me also make a standalone final trainer that doesn't depend on run_cv, producing the checkpoint. I'll reuse train_core but with Xte = a small held-out just for monitoring (or the whole set as calib). Actually for final training on ALL data, there's no held-out. I'll just train and save SWA weights. Let me write a dedicated function `train_final` in trainlib that returns the SWA (or raw) state_dict and norm, and saves. Let me add train_final to trainlib (won't affect running sweep4 process). train_final(cfg, X, Y): - compute norm on (X,Y) - train epochs with flip aug, wpow, SWA - at end, take swa_state (or raw), build model, load, BN-recalibrate on clean normalized X, return model + norm. Let me write it. I'll reuse logic. Let me add.

While sweep4 runs, let me write a dedicated final-training function that trains on all pooled data with SWA and recalibrates BatchNorm on clean data before saving.

str_replace_based_edit_tool

command: str_replace
path: /app/scratch/trainlib.py
old_str: def save_ckpt(path, sd, norm):
new_str: def train_final(cfg_over, X, Y, use="swa", verbose=True):
    """Train on ALL (X,Y); return (state_dict, norm) for chosen weights, BN-recalibrated."""
    snaps = [cfg_over["epochs"]]
    # Xte is unused for selection here; pass a small slice to keep code path happy.
    _, norm, swa_state = train_core(cfg_over, X, Y, X[:2], snaps)
    fmean, fstd, lmean, lstd = [t.to(DEV) for t in norm]
    cfg = load_cfg()
    model = build_model(cfg).to(DEV)
    if use == "swa" and swa_state is not None:
        model.load_state_dict({k: v.to(DEV) for k, v in swa_state.items()})
    calib_n = (X.to(DEV) - fmean) / fstd
    bn_recalibrate(model, calib_n, passes=5)
    if verbose:
        r = evaluate(model, X.to(DEV), Y.to(DEV), fmean, fstd, lmean, lstd)
        print(f"[train_final] in-sample rel/mae/mx = {r[0]:.4f}/{r[1]:.4f}/{r[2]:.4f}", flush=True)
    return {k: v.detach().cpu() for k, v in model.state_dict().items()}, tuple(t.cpu() for t in norm)


def save_ckpt(path, sd, norm):

output

The file /app/scratch/trainlib.py has been edited successfully.

Wait, train_final calls train_core which, at the snapshot epoch (the final epoch), will try to build a swa_model and predict on Xte (X[:2]) , fine. It returns swa_state. Good. But there's a subtlety: train_core's snapshot also does BN recalibration on calib_n (= Xtr since calib_x=None → calib=Xtr=X). That's fine. But note: when use='swa', I reload swa_state and recalibrate BN on full X. Good. Let me also double check train_core returns swa_state even when snapshot logic runs. Yes, returns (snaps, norm, swa_state). Now, one concern: in train_final I pass X[:2] as Xte , but train_core normalizes Xte with fmean/fstd and predicts; harmless. Let me verify the module still imports and a tiny smoke test of train_final with few epochs works (in a separate quick run, but GPU is busy with sweep4). Running another GPU process concurrently is fine on H100 (plenty memory), though it competes for compute and slows sweep4. Let me do a very short smoke test to validate code correctness without much compute. Actually, let me wait for sweep4 to not interfere with timing measurements. But I can do a quick CPU-less smoke on GPU with tiny epochs; it'll be quick. Let me run a 20-epoch train_final smoke test. Actually, running concurrently will slow sweep4 and muddy its timing, but correctness of sweep4 results is unaffected. Let me do a quick smoke test (epochs=20) to catch bugs early. Let me smoke test train_final and save_ckpt + reload path that mimics the grader. Let me also write the grader-mimicking evaluation to validate the saved checkpoint loads into the fixed architecture and produces correct outputs.

Let me do a quick smoke test (tiny epochs) to validate `train_final`, checkpoint saving, and grader-style reloading work end-to-end.

bash

cd /app/scratch && cat > grade_check.py << 'EOF'
# Mimic the grader: build model from task_config, load checkpoint, apply norm, predict, de-standardize.
import torch, json, sys
from pathlib import Path
sys.path.insert(0,'/app')
from candidate_model import build_model

def load_cfg():
    return json.load(open('/app/task_config.json'))

def predict_with_ckpt(ckpt_path, X):
    cfg=load_cfg(); m=build_model(cfg)
    ck=torch.load(ckpt_path, weights_only=True)
    m.load_state_dict(ck['state_dict']); m.eval()
    fmean=ck['feat_mean'].view(1,1,3); fstd=ck['feat_std'].view(1,1,3)
    lmean=ck['label_mean'].view(1,1); lstd=ck['label_std'].view(1,1)
    xn=(X-fmean)/fstd
    with torch.no_grad():
        out=torch.cat([m(xn[i:i+64]) for i in range(0,xn.shape[0],64)],0)
    return out*lstd+lmean

def metrics(pred,true):
    e=(pred-true).squeeze(1); t=true.squeeze(1)
    return (float(torch.sqrt((e**2).sum())/torch.sqrt((t**2).sum())),
            float(e.abs().mean()), float(e.abs().max()))

if __name__=='__main__':
    ck=sys.argv[1]
    for s in ['train','val','public_test']:
        d=torch.load(f'/app/data/{s}/samples.pt',weights_only=True)
        X=d['features'].float(); Y=d['labels'].float()
        p=predict_with_ckpt(ck,X); m=metrics(p,Y)
        print(f'  {s:12s} rel/mae/mx = {m[0]:.4f}/{m[1]:.4f}/{m[2]:.4f}')
EOF
echo "grade_check.py written"
# quick smoke test of train_final with tiny epochs
python -c "
import torch, trainlib as T
trX,trY=T.load_split('train'); vaX,vaY=T.load_split('val'); ptX,ptY=T.load_split('public_test')
X=torch.cat([trX,vaX,ptX],0); Y=torch.cat([trY,vaY,ptY],0)
cfg=dict(seed=1, epochs=30, bs=16, lr=1.5e-3, wd=1e-4, warmup=3, loss='mse', isotropic=True,
         aug=dict(flip=True), wpow=2.0, swa_start=20, swa_lr=2e-4)
sd,norm=T.train_final(cfg, X, Y, use='swa')
T.save_ckpt('/app/scratch/smoke.pt', sd, norm)
print('saved smoke.pt')
"
echo '--- grader-style check on smoke.pt ---'
python grade_check.py /app/scratch/smoke.pt

output

grade_check.py written
[train_final] in-sample rel/mae/mx = 0.4745/0.3309/1.0130
saved smoke.pt
--- grader-style check on smoke.pt ---
  train        rel/mae/mx = 0.4631/0.3174/1.0130
  val          rel/mae/mx = 0.5018/0.3789/0.8167
  public_test  rel/mae/mx = 0.4869/0.3193/0.8999

The smoke test works end-to-end (30 epochs → poor metrics as expected, but the pipeline is correct). The in-sample rel matches the grader-style check on train (0.47 vs 0.46, close , small diff due to BN recalibration timing). The checkpoint loads into the fixed architecture and produces correct-shaped outputs. Good. Now let me check sweep4 progress.

The full pipeline works , checkpoint saves and reloads into the fixed architecture with correct output shapes. Let me check sweep4 progress.

bash

cd /app/scratch && cat sweep4.log; pgrep -f sweep4.py >/dev/null && echo "[running]" || echo "[done]"

output

[running]

No output yet , first config (2000 epochs × 6 folds) still running, my concurrent smoke test slowed it. Let me wait. Each config ~400s → 3 configs ~20 min. Let me wait ~5min and check.

sweep4's first config is still running (my concurrent smoke test slowed it slightly). Let me wait.

bash

cd /app/scratch && sleep 290 && cat sweep4.log; pgrep -f sweep4.py >/dev/null && echo "[running]" || echo "[done]"

output

[running]

bash

cd /app/scratch && sleep 200 && cat sweep4.log; pgrep -f sweep4.py >/dev/null && echo "[running]" || echo "[done]"

output

[wpow=2.0] (473s)
     ep1200 raw: rel=0.1950 mae=0.1160 mx=0.6655
     ep1200 swa: rel=0.4035 mae=0.2541 mx=1.0899
     ep1500 raw: rel=0.1883 mae=0.1120 mx=0.6872
     ep1500 swa: rel=0.3869 mae=0.2416 mx=1.0717
     ep1800 raw: rel=0.1838 mae=0.1102 mx=0.5996
     ep1800 swa: rel=0.3751 mae=0.2312 mx=1.0388
     ep2000 raw: rel=0.1936 mae=0.1193 mx=0.6272
     ep2000 swa: rel=0.3691 mae=0.2262 mx=1.0238
[running]

Hmm, SWA is much WORSE (0.37-0.40) than raw (0.18-0.19)! That's surprising. The SWA starting at ep1200 with swa_lr=2e-4 produces bad results. Why? The issue: SWA averages weights across epochs 1200-2000. But with BatchNorm and a model still changing, the averaged weights might be in a bad region. Also swa_lr=2e-4 constant might be too high, causing the weights to wander across different basins that don't average well. Or the SWA BN recalibration has an issue. But raw is great: ep1800 raw rel=0.1838, mae=0.110, mx=0.600! Actually wait , the SWA being ~0.37 (close to mean-predictor-ish but not quite) suggests the averaged weights regress to mean, similar to EMA before. So weight averaging for THIS architecture/problem regresses heavy brackets to mean → bad for rel L2. The raw model is better. So: SWA and EMA both hurt here. I'll use the RAW model with well-chosen epoch. The best raw is around ep1800 (rel 0.184). But raw has epoch-to-epoch noise (ep2000 raw jumped to 0.194 from 0.184 at ep1800). So the question is how to pick a robust final checkpoint. Options: 1. Pick a fixed epoch (e.g., 1800) based on CV , but noise means the exact epoch matters. 2. Average PREDICTIONS across multiple seeds (can't , single model). 3. Use the raw model but reduce noise by... training with a lower final LR (cosine to near-zero) so the last epochs are stable. Currently cosine decays to swa_lr region? No , with swa_start set, the cosine decays to swa_start then constant swa_lr. Without SWA, I should let cosine decay all the way to ~0 so the final epochs are stable (low LR → small updates → stable weights). That would make the final-epoch model well-defined and reduce noise. Let me reconsider the LR schedule. In sweep4, swa_start=1200 caused cosine to reach minimum at ep1200 then constant 2e-4 for 1200-2000. The raw model at ep1800 (during constant 2e-4 phase) is good. But constant 2e-4 still causes wiggle. Better plan for final: pure cosine from lr=1.5e-3 down to ~0 over all epochs (no SWA, no constant tail). The final epochs have tiny LR → stable convergence. Pick total epochs via CV. Let me test pure cosine to 0 over e.g. 1800 epochs, flip-only, wpow 2, and look at raw at final epochs. The last-epoch model should be stable and good. Actually, let me reconsider the earlier sweeps: they used cosine to 0 (no swa_start) over `epochs`. E.g., fliponly_w2 epochs=1200 cosine→0 at 1200, raw rel 0.195 at ep1200. And sweep4 wpow2 with swa_start=1200 then constant 2e-4: raw improved to 0.184 at 1800. So the constant 2e-4 tail + more epochs helped raw reach 0.184. Hmm. So raw benefits from more training at moderate LR. Let me think about what gives the best, most robust raw model: - More epochs help (0.195@1200 → 0.184@1800). - But there's noise. Strategy for robustness: Instead of SWA over weights (which regresses to mean), I can reduce variance by choosing a config and epoch via CV, then for the FINAL model, since I train on all 148 data (more than CV's 110/fold), it should be at least as good. For picking the epoch robustly, I'll look at the CV curve averaged and pick an epoch in the stable low region. From sweep4 wpow2: ep1500-2000 raw all ~0.184-0.195. I'll target ~1800. But the epoch-to-epoch noise concern for the FINAL single model: if I train to exactly ep1800 on all data, I might hit a noisy point. To mitigate, I can let cosine decay to a very small LR at the end so the final weights are stable (small updates). Let me test: pure cosine to 0 over 1800 epochs , does raw at the end match ~0.184 and is it stable? Let me run a focused confirmation: flip-only, wpow 2 and 2.5, pure cosine to 0, epochs 1800, snapshots [1600,1700,1800] to see stability near the end. Also try wpow 2.5/3 which might help heavy brackets more. Also I realize I should double check the earlier claim that SWA/EMA regress to mean , the mae for SWA is 0.22-0.25 which is near mean-predictor mae (0.30 for val, but pooled ~0.25). So yes SWA ≈ regressed. Confirmed avoid. …[truncated]

Key finding: **SWA regresses heavy brackets toward the mean** (rel 0.37 vs raw 0.18), just like EMA , weight averaging hurts this problem. The **raw model is far better** (ep1800 raw: rel 0.184, MAE 0.110, max 0.60). I'll drop SWA/EMA entirely. Let me wait for the wpow 2.5/3.0 raw results.

bash

cd /app/scratch && sleep 280 && cat sweep4.log; pgrep -f sweep4.py >/dev/null && echo "[running]" || echo "[done]"

output

[wpow=2.0] (473s)
     ep1200 raw: rel=0.1950 mae=0.1160 mx=0.6655
     ep1200 swa: rel=0.4035 mae=0.2541 mx=1.0899
     ep1500 raw: rel=0.1883 mae=0.1120 mx=0.6872
     ep1500 swa: rel=0.3869 mae=0.2416 mx=1.0717
     ep1800 raw: rel=0.1838 mae=0.1102 mx=0.5996
     ep1800 swa: rel=0.3751 mae=0.2312 mx=1.0388
     ep2000 raw: rel=0.1936 mae=0.1193 mx=0.6272
     ep2000 swa: rel=0.3691 mae=0.2262 mx=1.0238
[running]

Still running wpow2.5. The concurrent processes earlier slowed things. Let me wait more. Each config ~470s; wpow2.5 and 3.0 remaining → ~15min total. Let me wait. Actually, while waiting, let me reconsider whether I should reduce the SWA overhead. The sweep computes swa each snapshot which is wasteful now that I know SWA is bad. But it's running; let me just wait. Let me wait ~5 min.

Still running wpow=2.5. Let me wait for the remaining raw results.

bash

cd /app/scratch && sleep 290 && sed -n '10,40p' sweep4.log; pgrep -f sweep4.py >/dev/null && echo "[running]" || echo "[done]"

output

[wpow=2.5] (410s)
     ep1200 raw: rel=0.2134 mae=0.1313 mx=0.7223
     ep1200 swa: rel=0.4464 mae=0.2900 mx=1.1445
     ep1500 raw: rel=0.2085 mae=0.1312 mx=0.6869
     ep1500 swa: rel=0.4354 mae=0.2809 mx=1.1257
     ep1800 raw: rel=0.2023 mae=0.1237 mx=0.6440
     ep1800 swa: rel=0.4215 mae=0.2694 mx=1.1058
     ep2000 raw: rel=0.2076 mae=0.1277 mx=0.6285
     ep2000 swa: rel=0.4139 mae=0.2628 mx=1.0962
[running]

wpow=2.5 is slightly worse than wpow=2.0 (0.202 vs 0.184 at ep1800). So wpow=2.0 is better. Let me wait for wpow=3.0 but it'll likely be worse. So the best config is: flip-only, wpow=2.0, lr=1.5e-3, wd=1e-4, and the raw model around ep1800 (with the constant 2e-4 tail from swa_start=1200 , wait, in sweep4 the schedule had swa_start=1200 and swa_lr=2e-4, so cosine decayed to ep1200 then constant 2e-4 for the raw model training too, since lr_at returns swa_lr when ep>=swa_start regardless of raw/swa). So the raw model in sweep4 was trained with cosine-to-ep1200 then constant 2e-4. That gave raw 0.184@1800. So the LR schedule that produced the good raw result: cosine from 1.5e-3 (warmup 20) down over epochs 20→1200, then constant 2e-4 from 1200→2000. Good to know. I'll replicate that for final, but WITHOUT using SWA weights , just take the raw model at the final epoch. But which epoch exactly for raw? ep1800 was best (0.184), ep2000 slightly worse (0.194). There's noise. For the final model on all data, I want a stable endpoint. Idea: extend constant-LR tail and decay to near-zero at the very end to stabilize. Or: I'll just pick ep1800 as target but add a short cosine decay from the constant tail to ~0 in the final ~200 epochs so the endpoint is stable. Actually, let me reconsider the noise. The noise between 1800 and 2000 (0.184 vs 0.194) at constant lr=2e-4 is the weights wiggling. If I decay LR to near 0 by the final epoch, the endpoint is wherever it settles , stable but could be anywhere in the wiggle range. Alternative robust approach: Since I can only ship one model, let me reduce endpoint variance by training with a final cosine decay to ~0, and choose total epochs so the decay lands around the good region. Let me design the final schedule: - warmup 20 - cosine from 1.5e-3 to ~1e-4 over epochs 20→1600 - then cosine (or linear) from 1e-4 to ~0 over 1600→2000? Hmm, getting complicated. Let me instead just test a clean schedule via CV: pure cosine 1.5e-3→0 over N epochs, flip-only, wpow2, and see the raw endpoint stability for N in {1600, 2000}. If pure cosine-to-0 gives a stable good endpoint (~0.185), I'll use that , it's simplest and the endpoint is well-defined (lowest LR = most stable). Let me wait for wpow3 to finish, then run this final confirmation. Actually, to save time, let me kill sweep4 after wpow3 or now (we have enough info). Let me wait for wpow=3 to confirm it's worse, then launch the final schedule confirmation. Actually I'm fairly sure wpow2 is best. Let me just wait a little for wpow3 then proceed. Let me check in ~5 min, or proceed now. Let me launch the final-schedule CV confirmation now in parallel won't work well (GPU contention). Let me wait for sweep4 to finish first.

wpow=2.0 remains best (0.184 vs 0.202 for 2.5 at ep1800). The good raw result used cosine decay to ep1200 then a constant 2e-4 tail. Let me wait for wpow=3.0 to finish, then confirm the final LR schedule.

bash

cd /app/scratch && sleep 250 && sed -n '19,30p' sweep4.log; pgrep -f sweep4.py >/dev/null && echo "[running]" || echo "[done]"

output

[wpow=3.0] (397s)
     ep1200 raw: rel=0.2325 mae=0.1376 mx=0.8617
     ep1200 swa: rel=0.4848 mae=0.3227 mx=1.1336
     ep1500 raw: rel=0.2151 mae=0.1290 mx=0.8105
     ep1500 swa: rel=0.4699 mae=0.3125 mx=1.1014
     ep1800 raw: rel=0.2106 mae=0.1255 mx=0.7854
     ep1800 swa: rel=0.4538 mae=0.3006 mx=1.0849
     ep2000 raw: rel=0.2196 mae=0.1300 mx=0.7864
     ep2000 swa: rel=0.4471 mae=0.2954 mx=1.0861
[done]

wpow=3.0 is worse (0.21). So wpow=2.0 is the sweet spot. Summary of best config (6-fold CV on 132 pooled): - flip-only augmentation, wpow=2.0, lr=1.5e-3, wd=1e-4, warmup=20 - schedule: cosine 1.5e-3→ down to ep1200, then constant 2e-4 - raw model @ ep1800: rel=0.184, mae=0.110, mx=0.600 Now, let me determine the best final schedule. I want to confirm stability and possibly squeeze more. Let me run a final confirmation CV comparing: - A: the sweep4 schedule (swa_start=1200, swa_lr=2e-4) raw at ep 1600/1700/1800/1900 , to see the stable window and pick epoch. - B: pure cosine to 0 over 1800 epochs , endpoint stability. - C: maybe lr 2e-3 or different wd. But also I should reduce variance of my CV estimate itself. Currently single seed=0 fold split. Let me also run seed=1 split to check robustness of the ~0.184 number. Given budget, let me run ONE more CV experiment that: (a) uses the chosen schedule (cosine→ep1200 then const 2e-4, total 1900), flip-only wpow2, eval raw at [1500,1600,1700,1800,1900], across TWO fold-seeds (0 and 1) to average. Actually run_cv takes a single seed. Let me just run seed=0 and seed=1 for the winning config and average, focusing on raw only (skip swa to save time: eval_which=['raw']). Let me also test a slightly longer constant tail / decay-to-zero-at-end variant. I'll include two schedule variants: - sched A: swa_start=1200, swa_lr=2e-4, epochs=1900 (constant tail) - sched B: swa_start=1400, swa_lr=5e-4, epochs=1900 (higher tail, later) - sched C: pure cosine to 0, epochs=1800 (swa_start=None) eval raw only, seed 0. Pick best & most stable. Keep it to ~3 configs, each ~ (1900 ep flip-only raw-only, no swa eval → faster). raw-only eval is cheap. ~6 folds × 1900 ep. Flip-only 2000ep earlier was ~400-470s WITH swa eval. Without swa eval, faster, ~300s each. 3 configs ~15min. Let me launch sweep5. Actually, let me be efficient and combine the schedule comparison and a second seed for the best one. Let me do: - configs: A (const tail 2e-4 from 1200), C (pure cosine to 0), and A with seed=1. Let me just run these 3. Let me write sweep5.

wpow=2.0 is confirmed optimal (2.5→0.202, 3.0→0.211). Best CV: **flip-only, wpow=2, rel-L2 0.184, MAE 0.110, max 0.60**, decisively beating the ridge baseline (~0.21–0.28). Now let me finalize the LR schedule and check stability/robustness across fold seeds (raw-only eval, skipping the now-unneeded SWA).

bash

cd /app/scratch && cat > sweep5.py << 'EOF'
import torch, trainlib as T, time
trX,trY=T.load_split('train'); vaX,vaY=T.load_split('val')
X=torch.cat([trX,vaX],0); Y=torch.cat([trY,vaY],0)
snaps=[1400,1500,1600,1700,1800,1900]
common=dict(epochs=1900, bs=16, lr=1.5e-3, wd=1e-4, warmup=20, loss='mse',
            isotropic=True, aug=dict(flip=True), wpow=2.0, eval_which=['raw'])
trials = [
  ('A_consttail_s0', dict(seed=0, swa_start=1200, swa_lr=2e-4)),
  ('C_cosine0_s0',  dict(seed=0)),
  ('A_consttail_s1', dict(seed=1, swa_start=1200, swa_lr=2e-4)),
  ('A_consttail_s2', dict(seed=2, swa_start=1200, swa_lr=2e-4)),
]
for name,over in trials:
    cfg=dict(common); cfg.update(over)
    fold_seed = over.get('seed',0)
    t=time.time(); res=T.run_cv(cfg,(X,Y),n_folds=6,snapshot_epochs=snaps,seed=fold_seed)
    best=min(((res[(e,'raw')][0],e) for e in snaps))
    print(f'[{name}] ({time.time()-t:.0f}s) BEST rel={best[0]:.4f} @ep{best[1]}',flush=True)
    for e in snaps:
        r=res[(e,'raw')]; print(f'     ep{e:4d} rel={r[0]:.4f} mae={r[1]:.4f} mx={r[2]:.4f}',flush=True)
EOF
nohup python sweep5.py > sweep5.log 2>&1 &
echo "launched $!"

output

launched 657

While sweep5 runs, let me think about whether there are further gains available and prepare the final training. One more idea to improve: the heavy-bracket underprediction. wpow=2 helps. But maybe I can do better on max-error and rel-L2 by a different mechanism. However, higher wpow (2.5, 3) hurt overall rel. So wpow=2 balances. Another idea: the input normalization. I used isotropic. Let me not over-engineer; isotropic is principled. Another thought: Does averaging predictions from multiple FOLD models help the final? No , final is one model on all data. Let me also consider: could I train the final model on all 148 samples and expect ~0.184 or better on the hidden set? The CV estimate (0.184) is on held-out folds of the 132 train+val. The hidden test is similar distribution. Training final on 148 (slightly more) should give similar or marginally better generalization. The public_test peek earlier (with good configs) showed ~0.28 but that was with worse configs/jitter. Let me verify the final model's public_test performance with the good config as a sanity check (public_test is part of my final training data though, so that's in-sample , not a clean check). Actually, to get a clean estimate of how the FINAL recipe does on truly held-out data, my 6-fold CV on 132 is the cleanest (0.184). The public_test 16 samples: if I train on train+val only (132) and test on public_test (16), that's a clean held-out estimate too. Let me do that as a final confirmation before committing: train final recipe on 132 (train+val), evaluate on public_test (16). That gives an independent check on 16 unseen samples. If it's ~0.18-0.25, great. Let me plan the final deliverable: 1. Confirm schedule via sweep5. 2. Clean held-out check: train on train+val (132), eval public_test (16). 3. Final model: train on ALL 148 with chosen recipe, save to /app/outputs/model.pt. 4. Validate with grade_check.py (in-sample, just to confirm pipeline) and ensure format correct. For the final epoch choice: I'll pick based on sweep5's stable window. Likely ep ~1700-1800 with const tail. To reduce endpoint noise, I might average... no. I'll just pick the epoch with best CV and verify. Actually, here's a cleaner idea to reduce endpoint variance for the FINAL single model WITHOUT hurting (unlike SWA which regressed to mean): Since SWA hurt because it averaged over too wide/constant-LR wandering, maybe the problem was averaging during constant moderate LR. Weight averaging over a SHORT window at the END with LOW LR (where weights are in a tight basin) might not regress to mean. But risky. Given SWA clearly hurt in tests, I'll avoid it and just use the raw final-epoch model with a schedule that decays LR low at the end for a stable endpoint. Let me reconsider: the good raw result (0.184) was at ep1800 during CONSTANT 2e-4 tail (not decayed). If I decay to 0 at the end, does it stay good? sweep5's "C_cosine0" tests pure cosine to 0 over 1900 , I'll see. If C is stable and good, I'll use a decay-to-low schedule for a well-defined endpoint. Let me wait for results. Let me wait ~5 min. Meanwhile, let me also prepare the final training script file /app/scratch/make_final.py so I can run it once config is locked. Final script logic: - pool train+val+public_test (148) - cfg = best (flip-only, wpow2, schedule TBD) - train_final(cfg, X, Y, use='raw') # use raw weights, BN-recalibrated on clean X - save to /app/outputs/model.pt - run grade_check for sanity But train_final currently returns the model at the FINAL epoch (snapshot = epochs). With use='raw', it takes model.state_dict() after training and BN-recalibrates. Wait , train_final calls train_core which returns swa_state but for raw I need the final model weights. Currently train_final builds a fresh model and loads swa_state for 'swa', but for 'raw' it uses the freshly-built model WITHOUT loading trained weights! Bug: for use!='swa', model is just initialized. Let me fix train_final to return the trained raw weight …[truncated]

While sweep5 runs, I spotted a bug in `train_final`: for `use='raw'` it doesn't load the trained weights. Let me fix `train_core` to also return the final raw state_dict, and update callers.

str_replace_based_edit_tool

command: str_replace
path: /app/scratch/trainlib.py
old_str:             snaps[ep + 1] = entry
    return snaps, (fmean.cpu(), fstd.cpu(), lmean.cpu(), lstd.cpu()), swa_state
new_str:             snaps[ep + 1] = entry
    final_raw = {k: v.detach().clone() for k, v in model.state_dict().items()}
    return snaps, (fmean.cpu(), fstd.cpu(), lmean.cpu(), lstd.cpu()), swa_state, final_raw

output

The file /app/scratch/trainlib.py has been edited successfully.

bash

cd /app/scratch && python - << 'EOF'
import re, io
src=open('trainlib.py').read()
# update run_cv unpacking
src=src.replace("snaps, _, _ = train_core(cfg_over, Xtr, Ytr, Xte, snapshot_epochs)",
                "snaps, _, _, _ = train_core(cfg_over, Xtr, Ytr, Xte, snapshot_epochs)")
# update train_final to use final_raw for 'raw'
old="""    snaps = [cfg_over["epochs"]]
    # Xte is unused for selection here; pass a small slice to keep code path happy.
    _, norm, swa_state = train_core(cfg_over, X, Y, X[:2], snaps)
    fmean, fstd, lmean, lstd = [t.to(DEV) for t in norm]
    cfg = load_cfg()
    model = build_model(cfg).to(DEV)
    if use == "swa" and swa_state is not None:
        model.load_state_dict({k: v.to(DEV) for k, v in swa_state.items()})"""
new="""    snaps = [cfg_over["epochs"]]
    # Xte is unused for selection here; pass a small slice to keep code path happy.
    _, norm, swa_state, final_raw = train_core(cfg_over, X, Y, X[:2], snaps)
    fmean, fstd, lmean, lstd = [t.to(DEV) for t in norm]
    cfg = load_cfg()
    model = build_model(cfg).to(DEV)
    if use == "swa" and swa_state is not None:
        model.load_state_dict({k: v.to(DEV) for k, v in swa_state.items()})
    else:
        model.load_state_dict({k: v.to(DEV) for k, v in final_raw.items()})"""
assert old in src
src=src.replace(old,new)
open('trainlib.py','w').write(src)
print("patched")
EOF
python -c "import importlib,trainlib; importlib.reload(trainlib); print('reimport OK')"

output

patched
reimport OK

Now let me re-run the smoke test of train_final with use='raw' to confirm the fix works (now it should load trained weights and give a reasonable in-sample fit even with 30 epochs). But sweep5 is running on GPU. A quick 30-epoch smoke won't hurt much. Let me run it quickly. Actually, let me avoid GPU contention disturbing sweep5 timing. The smoke test is fast (30 epochs). Let me run it; correctness matters more than sweep5 timing.

Let me verify the `train_final` fix with a quick smoke test (the raw path should now fit in-sample).

bash

cd /app/scratch && python -c "
import torch, trainlib as T
trX,trY=T.load_split('train')
cfg=dict(seed=1, epochs=60, bs=16, lr=1.5e-3, wd=1e-4, warmup=3, loss='mse', isotropic=True,
         aug=dict(flip=True), wpow=2.0)
sd,norm=T.train_final(cfg, trX, trY, use='raw')
T.save_ckpt('/app/scratch/smoke.pt', sd, norm)
" && python grade_check.py /app/scratch/smoke.pt

output

[train_final] in-sample rel/mae/mx = 0.4866/0.3491/0.8274
  train        rel/mae/mx = 0.4866/0.3490/0.8273
  val          rel/mae/mx = 0.5482/0.4253/0.7579
  public_test  rel/mae/mx = 0.5056/0.3500/0.7564

Now the in-sample (train) metric from train_final (0.4866) exactly matches the grade_check on train (0.4866) , the pipeline is consistent and the raw weights are properly loaded and saved. With only 60 epochs it's underfit (expected), but the consistency confirms correctness. Now let me wait for sweep5 to finish to lock the schedule and epoch. Let me check progress.

The pipeline is now consistent (train_final in-sample rel 0.4866 exactly matches grade_check's train metric), confirming raw weights save/load correctly. Let me check sweep5.

bash

cd /app/scratch && cat sweep5.log; pgrep -f sweep5.py >/dev/null && echo "[running]" || echo "[done]"

output

[running]

Still running, no output yet. The first config (1900 ep × 6 folds, raw-only) takes a while, and my smoke test competed for GPU. Let me wait ~5 min.

Let me wait for sweep5 results.

bash

cd /app/scratch && sleep 290 && cat sweep5.log; pgrep -f sweep5.py >/dev/null && echo "[running]" || echo "[done]"

output

[running]

bash

cd /app/scratch && sleep 120 && cat sweep5.log; pgrep -f sweep5.py >/dev/null && echo "[running]" || echo "[done]"

output

[A_consttail_s0] (398s) BEST rel=0.1838 @ep1800
     ep1400 rel=0.1907 mae=0.1164 mx=0.5850
     ep1500 rel=0.1883 mae=0.1120 mx=0.6872
     ep1600 rel=0.1856 mae=0.1111 mx=0.5494
     ep1700 rel=0.1867 mae=0.1112 mx=0.6338
     ep1800 rel=0.1838 mae=0.1102 mx=0.5996
     ep1900 rel=0.1896 mae=0.1127 mx=0.6276
[running]

A_consttail_s0 (constant 2e-4 tail): raw is stable in the 0.184-0.191 range across ep1400-1900, with best 0.1838 @ ep1800. The window ep1600-1800 is stable (~0.184-0.186). Good. Let me wait for C (pure cosine to 0) and the other seeds. The seed variation (s1, s2) will tell me how robust 0.184 is. Let me wait.

A_consttail is stable (rel 0.184–0.191) across ep1400–1900, best 0.184 at ep1800. Let me wait for the pure-cosine variant and the other fold-seeds to assess robustness.

bash

cd /app/scratch && sleep 290 && sed -n '8,40p' sweep5.log; pgrep -f sweep5.py >/dev/null && echo "[running]" || echo "[done]"

output

[running]

Hmm, sed -n '8,40p' shows nothing new beyond line 7 (the first config's 7 lines). So C config (pure cosine) is still running. Let me wait more and show the full log.

bash

cd /app/scratch && sleep 150 && cat sweep5.log; pgrep -f sweep5.py >/dev/null && echo "[running]" || echo "[done]"

output

[A_consttail_s0] (398s) BEST rel=0.1838 @ep1800
     ep1400 rel=0.1907 mae=0.1164 mx=0.5850
     ep1500 rel=0.1883 mae=0.1120 mx=0.6872
     ep1600 rel=0.1856 mae=0.1111 mx=0.5494
     ep1700 rel=0.1867 mae=0.1112 mx=0.6338
     ep1800 rel=0.1838 mae=0.1102 mx=0.5996
     ep1900 rel=0.1896 mae=0.1127 mx=0.6276
[C_cosine0_s0] (389s) BEST rel=0.1922 @ep1600
     ep1400 rel=0.1959 mae=0.1186 mx=0.6271
     ep1500 rel=0.1974 mae=0.1176 mx=0.7203
     ep1600 rel=0.1922 mae=0.1134 mx=0.6548
     ep1700 rel=0.1922 mae=0.1157 mx=0.6675
     ep1800 rel=0.1952 mae=0.1178 mx=0.6672
     ep1900 rel=0.1923 mae=0.1167 mx=0.6331
[running]

C (pure cosine to 0) gives ~0.192, slightly worse than A (const tail 2e-4) at ~0.184. So the constant 2e-4 tail is better. Makes sense , the model keeps learning fine details at moderate LR. Let me wait for seeds 1 and 2 of config A to confirm robustness of ~0.184. Let me wait.

The constant 2e-4 tail (A, ~0.184) beats pure cosine-to-0 (C, ~0.192). Let me wait for the other fold-seeds to confirm robustness.

bash

cd /app/scratch && sleep 290 && sed -n '15,50p' sweep5.log; pgrep -f sweep5.py >/dev/null && echo "[running]" || echo "[done]"

output

[A_consttail_s1] (377s) BEST rel=0.2075 @ep1800
     ep1400 rel=0.2119 mae=0.1244 mx=0.7695
     ep1500 rel=0.2150 mae=0.1253 mx=0.8226
     ep1600 rel=0.2147 mae=0.1248 mx=0.7924
     ep1700 rel=0.2152 mae=0.1266 mx=0.8205
     ep1800 rel=0.2075 mae=0.1245 mx=0.7297
     ep1900 rel=0.2090 mae=0.1249 mx=0.7949
[running]

Seed 1 (different fold split) gives ~0.208, a bit worse than seed 0 (0.184). This is expected variance from the random fold assignment (different 132→110/22 splits). The true generalization is somewhere around 0.18-0.21. Let me wait for seed 2. So across fold seeds: s0=0.184, s1=0.208. Average ~0.196. The metric has meaningful variance due to small data. My final model on all 148 should land in this ballpark on the hidden set. Let me wait for s2 to get a third estimate.

Fold-seed 1 gives ~0.208 (vs 0.184 for seed 0) , expected variance from small-data splits; true level ≈ 0.19–0.21. Let me get the third seed estimate.

bash

cd /app/scratch && sleep 250 && sed -n '22,50p' sweep5.log; pgrep -f sweep5.py >/dev/null && echo "[running]" || echo "[done]"

output

[running]

bash

cd /app/scratch && sleep 160 && sed -n '22,50p' sweep5.log; pgrep -f sweep5.py >/dev/null && echo "[running]" || echo "[done]"

output

[A_consttail_s2] (386s) BEST rel=0.2018 @ep1500
     ep1400 rel=0.2091 mae=0.1253 mx=0.7776
     ep1500 rel=0.2018 mae=0.1188 mx=0.7528
     ep1600 rel=0.2049 mae=0.1184 mx=0.7867
     ep1700 rel=0.2052 mae=0.1226 mx=0.7400
     ep1800 rel=0.2040 mae=0.1178 mx=0.8070
     ep1900 rel=0.2073 mae=0.1201 mx=0.8017
[done]

Three fold-seeds for config A (const tail 2e-4, flip-only, wpow2): - s0: 0.184 (mae 0.110, mx 0.60) - s1: 0.208 (mae 0.125, mx 0.73) - s2: 0.202-0.204 (mae 0.118, mx ~0.75-0.81) Average CV rel ≈ 0.198, mae ≈ 0.118, max ≈ 0.70. This is a robust estimate of generalization. Good , this beats the ridge baseline and the mean predictor decisively. The epoch window 1500-1800 is all stable and good. I'll target ep1700 as a central, stable choice for the final model (const 2e-4 tail). Now, a key idea to improve the FINAL single model beyond a single training run: reduce variance. Since fold-seed variance is ~0.18-0.21, the final model's quality depends on the particular training. I cannot ensemble into one checkpoint via weight-averaging (SWA hurt). But I have a better idea: I train the final model on ALL 148 samples. With more data than each CV fold (110), it should generalize slightly better. The epoch: use the stable window, ep~1700, const 2e-4 tail. To reduce the risk of landing on a bad seed, I can: 1. Train a few final candidates (different seeds) on train+val (132), evaluate each on public_test (16) as a clean held-out check, pick the best-generalizing seed, then... but that selects on 16 samples (noisy) and the final would be trained on 132 not 148. Alternatively: train several seeds on ALL 148, and pick the one with best CV-consistent behavior. But I can't evaluate on held-out if I train on all 148. Cleanest robust approach given constraints: - Do a clean held-out validation: train the final recipe on train+val (132) with a few seeds, evaluate each on public_test (16). This tells me the expected hidden-set performance and lets me sanity-check. - Then train the FINAL model on all 148 with the recipe and a seed. To pick the seed for the all-148 model, I can't directly validate. But seed variance of the MODEL (not the fold split) is likely smaller than fold-split variance. Let me measure model-seed variance: same fold split (seed 0 folds), different model init seed. Actually run_cv uses cfg seed for BOTH torch.manual_seed (model init + aug) and... no, fold split uses the `seed` arg passed to run_cv separately. In sweep5 I passed fold_seed = cfg seed, so both fold split and model init changed together. So s0/s1/s2 varied BOTH fold split and init. The variance includes both. For the FINAL all-148 model, there's no fold split; only model-init/aug seed matters. Let me estimate model-init variance by training on the SAME 132 (train+val) with different init seeds and evaluating on public_test (16) , clean held-out. This gives both an estimate of hidden performance and the init-seed spread. Let me do that as the final validation step: train recipe on 132 with seeds {0,1,2,3}, eval on public_test (16), report. Then train final on 148. Actually, an even better use of the data + robustness: What if the final deliverable is trained on all 148 and I choose a seed that I've validated generalizes well? Since I can't validate the all-148 model directly, I'll rely on the recipe being robust (CV ~0.20) and pick a seed that did well in the held-out-public check when trained on 132. The init-seed effect should transfer reasonably. Hmm, but training set differs (132 vs 148). Let me keep it simple and principled: Decision: - Final model = trained on ALL 148 samples, recipe A (flip-only, wpow2, lr1.5e-3→const2e-4 tail from ep1200, total ep1700, wd1e-4), seed chosen as the best from the public-holdout check. - Before that, run the public-holdout check (train on 132, eval 16) across a few seeds to (a) confirm ~0.18-0.22 generalization and (b) pick a good seed. Let me run that check now. I'll train on train+val (132), eval on public_test (16), for seeds 0,1,2,3, at ep1700 (and show 1600/1700/1800). This is a clean held-out test (public_test not in training). Actually, I realize using public_test to pick the seed is a mild form of selection on 16 samples, which could overfit to those 16. But the hidden test is different from public_test. …[truncated]

Three fold-seeds give a robust CV estimate: **rel-L2 ≈ 0.198, MAE ≈ 0.118, max ≈ 0.70** (seeds: 0.184/0.208/0.204), decisively beating ridge (~0.21–0.28) and the mean predictor (0.47). The epoch window 1500–1800 is stable. Now let me run a **clean held-out check**: train the final recipe on train+val (132) and evaluate on the untouched public_test (16), across seeds, to confirm real generalization before committing.

bash

cd /app/scratch && cat > holdout.py << 'EOF'
import torch, trainlib as T, time
trX,trY=T.load_split('train'); vaX,vaY=T.load_split('val'); ptX,ptY=T.load_split('public_test')
Xtr=torch.cat([trX,vaX],0); Ytr=torch.cat([trY,vaY],0)   # 132 train
snaps=[1500,1600,1700,1800]
base=dict(epochs=1800, bs=16, lr=1.5e-3, wd=1e-4, warmup=20, loss='mse', isotropic=True,
          aug=dict(flip=True), wpow=2.0, swa_start=1200, swa_lr=2e-4)
def met(pred,true):
    e=(pred-true).squeeze(1); t=true.squeeze(1)
    return (float(torch.sqrt((e**2).sum())/torch.sqrt((t**2).sum())), float(e.abs().mean()), float(e.abs().max()))
for seed in [0,1,2]:
    cfg=dict(base); cfg['seed']=seed
    t=time.time()
    snapres,_,_,_=T.train_core(cfg, Xtr, Ytr, ptX, snaps)
    print(f'[seed={seed}] ({time.time()-t:.0f}s) public_test held-out:',flush=True)
    for e in snaps:
        m=met(snapres[e]['raw'], ptY)
        print(f'     ep{e:4d} rel={m[0]:.4f} mae={m[1]:.4f} mx={m[2]:.4f}',flush=True)
EOF
nohup python holdout.py > holdout.log 2>&1 &
echo "launched $!"

output

launched 785

While that runs, let me prepare the final training script. The final model trains on ALL 148 samples. Let me write make_final.py now. Config for final: - flip-only aug, wpow=2.0, lr=1.5e-3, wd=1e-4, warmup=20 - swa_start=1200, swa_lr=2e-4 (this gives the cosine→ep1200 then const 2e-4 tail schedule; I'm NOT using SWA weights, just use='raw') - epochs=1700 (central stable point) - isotropic normalization - use='raw' Wait , train_final passes snaps=[epochs] and uses final_raw (the model at the final epoch). With epochs=1700 and swa_start=1200, the schedule is cosine to ep1200 then const 2e-4 until 1700. The final_raw is the model at ep1700. Good. But note: swa_state accumulates from ep1200 but we won't use it (use='raw'). Fine. Actually, accumulating SWA wastes a tiny bit of compute/memory but harmless. Let me set swa_start only for the LR schedule effect. Since swa_start triggers both the LR schedule AND swa accumulation, and I use raw, that's fine. Actually, I could instead implement the const-tail schedule without triggering SWA accumulation, but it's harmless. Keep as is. Let me write make_final.py. It will: 1. Pool all 148. 2. train_final(cfg, X, Y, use='raw') → sd, norm 3. save_ckpt('/app/outputs/model.pt', sd, norm) 4. Run grade_check on the saved model for sanity (in-sample + the three splits; note all are in-sample now). Let me also verify the output format matches requirements: keys state_dict, feat_mean, feat_std (len-3), label_mean, label_std (len-1). save_ckpt does: feat_mean.squeeze() → len 3, feat_std.squeeze() → I set feat_std as isotropic repeat(3), so squeeze → len 3. label_mean/std reshaped to (1). Good. Let me double-check compute_norm returns feat_std as repeat(3) (len 3) for isotropic , yes. And label_mean/std as labels.mean(dim=0) → shape [1], std [1]. save_ckpt reshapes to (1). Good. Let me write make_final.py but not run until holdout confirms. Actually I can write it now. Let me also reconsider the final epoch: CV best was ep1800 for s0 but ep1500 for s2; the window is flat. I'll pick ep1700 as a robust central value. But since I train on 148 (more data) vs 132 in CV, the optimal epoch might shift slightly later (more data, more steps per epoch: 148/16≈10 steps/epoch vs 132/16≈9). Marginal. ep1700 is fine. Actually, the schedule's const-tail means any epoch in 1500-1800 is fine. I'll use 1700. Let me write the final script.

While the held-out check runs, let me write the final training script (trains on all 148 samples with the locked recipe, using raw weights).

str_replace_based_edit_tool

command: create
path: /app/scratch/make_final.py
file_text: import torch, trainlib as T, sys

# Pool ALL labeled data (train + val + public_test) for the final fit.
trX, trY = T.load_split('train')
vaX, vaY = T.load_split('val')
ptX, ptY = T.load_split('public_test')
X = torch.cat([trX, vaX, ptX], 0)
Y = torch.cat([trY, vaY, ptY], 0)
print('final training pool:', X.shape, Y.shape, flush=True)

SEED = int(sys.argv[1]) if len(sys.argv) > 1 else 0
cfg = dict(seed=SEED, epochs=1700, bs=16, lr=1.5e-3, wd=1e-4, warmup=20,
           loss='mse', isotropic=True, aug=dict(flip=True), wpow=2.0,
           swa_start=1200, swa_lr=2e-4)

sd, norm = T.train_final(cfg, X, Y, use='raw')
T.save_ckpt('/app/outputs/model.pt', sd, norm)
print('WROTE /app/outputs/model.pt', flush=True)

# sanity: inspect saved payload
ck = torch.load('/app/outputs/model.pt', weights_only=True)
print('keys:', list(ck.keys()))
print('feat_mean', ck['feat_mean'].shape, ck['feat_mean'].tolist())
print('feat_std ', ck['feat_std'].shape, ck['feat_std'].tolist())
print('label_mean', ck['label_mean'].shape, ck['label_mean'].tolist())
print('label_std ', ck['label_std'].shape, ck['label_std'].tolist())

output

New file created successfully at /app/scratch/make_final.py

bash

cd /app/scratch && sleep 170 && cat holdout.log; pgrep -f holdout.py >/dev/null && echo "[running]" || echo "[done]"

output

[seed=0] (83s) public_test held-out:
     ep1500 rel=0.3727 mae=0.2079 mx=0.7690
     ep1600 rel=0.3119 mae=0.1693 mx=0.6126
     ep1700 rel=0.2887 mae=0.1612 mx=0.5773
     ep1800 rel=0.3122 mae=0.1638 mx=0.6567
[seed=1] (78s) public_test held-out:
     ep1500 rel=0.3399 mae=0.1959 mx=0.6430
     ep1600 rel=0.2887 mae=0.1751 mx=0.5061
     ep1700 rel=0.3139 mae=0.1872 mx=0.5692
     ep1800 rel=0.3269 mae=0.1808 mx=0.6725
[running]

Interesting. On the public_test (16 held-out), the rel L2 is ~0.29-0.37, noticeably worse than the CV estimate (~0.20). Why the discrepancy? Possible reasons: 1. public_test is a different/harder distribution than train+val. Recall earlier: public_test mass mean 0.73, std 0.33 (narrower range, max 1.45), while train mean 0.75 std 0.39 (max 2.2). public_test has no very-heavy brackets. So rel L2 denominator ||y|| is smaller for public (masses smaller), making rel L2 larger for the same absolute errors. Also public might contain shapes less represented in train. 2. The MAE on public (~0.16-0.18) is similar to CV MAE (~0.12-0.13) but a bit higher. Max is ~0.57-0.67, similar. So absolute errors are comparable; the higher rel L2 is partly the smaller denominator. Let me compute: public ||y|| with 16 samples mean 0.73. CV pooled ||y|| with 132 samples includes heavy ones. rel = ||e||/||y||. If ||e|| similar per-sample but public has smaller ||y|| per sample and no heavy samples to inflate denominator, rel is higher. Let me reconsider: is the model generalizing worse on public, or is it just the metric's denominator? The MAE (0.16 vs 0.12) is somewhat worse too. Partly, public_test has 16 samples and some may be hard. Regardless, this is informative: the true hidden-set rel L2 might be ~0.20-0.30 depending on the distribution. My CV (0.20) might be optimistic if the hidden set resembles public_test. Key question: can I reduce the public_test error? The held-out public check shows ep1700 seed0 = 0.289, seed1 ep1600 = 0.289. The best epochs vary. The errors are dominated by... let me look at which public samples are bad. But more importantly, I should make the final model as robust as possible. Since the final model trains on ALL 148 (including these 16 public_test samples), it will fit them too , but that doesn't tell me hidden-set performance. The honest estimates I have: - 6-fold CV on 132 (train+val): rel ~0.18-0.21 → but these folds are from train+val distribution. - public_test held-out (train on 132, test on 16): rel ~0.29. The hidden test set is separate. Its distribution is unknown but likely similar to the overall SimJEB dataset. The truth is probably between these estimates. Let me think about whether I can improve generalization further. Ideas: 1. The model may benefit from MORE data. Training final on 148 (vs 110 per CV fold or 132) gives more data → likely better on hidden set. Good. 2. Distribution shift: public_test lacks heavy brackets. If the hidden set also has a different mass distribution, my wpow=2 weighting (which emphasizes heavy) might not be ideal for a set without heavy samples. But wpow=2 improved CV overall. For robustness across distributions, wpow=2 is reasonable. Let me reconsider wpow on the public holdout specifically. The holdout used wpow=2. Let me not over-tune to public (16 samples). 3. Could averaging predictions over the stable epoch window help? I can't , single model. But I could pick the epoch that's most robust across seeds/splits. CV said 1500-1800 all similar; public said 1600-1700 best. I'll target ep1700 (or 1650). Actually, let me reconsider the discrepancy more carefully by checking per-sample public errors and whether a specific sample dominates. Let me wait for seed2 and then investigate. Actually, let me just also get an aggregate: the model generalizes to ~0.16-0.18 MAE and ~0.6 max on public. The mean-predictor on public was rel 0.40, mae 0.26. So we're well better than baseline (0.29 vs 0.40 rel, 0.16 vs 0.26 mae). Good , solid improvement. Let me wait for seed2 of holdout, then make a decision. Actually, I think the recipe is solid. Let me also consider: should I combine the CV-informed decision with training on all data and just ship it? Yes. But let me reduce variance/risk on the final single model. Here's a robust idea that IS compatible with one checkpoint and does NOT regress to mean like SWA: I can't ensemble weights. So I ship one raw model. To de-risk the …[truncated]

The public_test held-out rel-L2 (~0.29) is higher than CV (~0.20) , largely because public_test has a **narrower mass range (no heavy brackets, max 1.45 vs 2.2)**, shrinking the rel-L2 denominator; the MAE (~0.16) and max (~0.58) are only modestly higher. We still clearly beat the baseline (mean-predictor: rel 0.40, MAE 0.26). Let me wait for seed 2, then verify whether BN recalibration (used by `train_final` but not in my CV checks) helps or hurts.

bash

cd /app/scratch && sleep 60 && sed -n '11,20p' holdout.log; pgrep -f holdout.py >/dev/null && echo "[running]" || echo "[done]"

output

[seed=2] (81s) public_test held-out:
     ep1500 rel=0.3498 mae=0.2039 mx=0.7139
     ep1600 rel=0.2905 mae=0.1783 mx=0.5338
     ep1700 rel=0.3274 mae=0.1969 mx=0.5868
     ep1800 rel=0.3379 mae=0.1875 mx=0.6404
[done]

Seed 2 public held-out: ep1600 rel 0.29, ep1700 0.33. The ep1600 seems consistently good across seeds (0.31, 0.29, 0.29). ep1700 varies (0.29, 0.31, 0.33). So ep1600 might be a slightly more robust choice for the const-tail schedule. But these are on 16 samples , noisy. Summary public held-out (train on 132): - seed0: ep1600 0.312, ep1700 0.289 - seed1: ep1600 0.289, ep1700 0.314 - seed2: ep1600 0.291, ep1700 0.327 ep1600 avg ≈ 0.297, ep1700 avg ≈ 0.310. So ep1600 slightly better/more stable on public. Combined with CV (1500-1800 all similar, best around 1600-1800), I'll pick ep1650 as a compromise, or ep1600. Let me use ep1650. Now let me verify BN recalibration effect. Let me write a quick test: train on 132 (seed 0), then compare public_test metrics for (a) raw model as-is (training BN stats) vs (b) BN-recalibrated on clean 132. Use train_final-like path. Let me just do it directly. Let me test this quickly.

Seed 2 confirms ep1600 is robust across seeds (0.312/0.289/0.291) , slightly more stable than ep1700. Now let me verify whether the **BN recalibration** that `train_final` applies helps or hurts on held-out public_test (since my CV checks didn't recalibrate).

bash

cd /app/scratch && python -c "
import torch, trainlib as T, copy
trX,trY=T.load_split('train'); vaX,vaY=T.load_split('val'); ptX,ptY=T.load_split('public_test')
Xtr=torch.cat([trX,vaX],0); Ytr=torch.cat([trY,vaY],0)
cfg=dict(seed=0, epochs=1650, bs=16, lr=1.5e-3, wd=1e-4, warmup=20, loss='mse', isotropic=True,
         aug=dict(flip=True), wpow=2.0, swa_start=1200, swa_lr=2e-4)
snaps=[1650]
snapres,norm,_,final_raw=T.train_core(cfg, Xtr, Ytr, ptX, snaps)
fmean,fstd,lmean,lstd=[t.to(T.DEV) for t in norm]
def met(pred,true):
    e=(pred-true).squeeze(1); t=true.squeeze(1)
    return (float(torch.sqrt((e**2).sum())/torch.sqrt((t**2).sum())), float(e.abs().mean()), float(e.abs().max()))
# (a) raw training-BN stats
print('raw (train BN stats):   ', [round(x,4) for x in met(snapres[1650]['raw'], ptY)])
# (b) recalibrated BN on clean 132
m=T.build_model(T.load_cfg()).to(T.DEV); m.load_state_dict({k:v.to(T.DEV) for k,v in final_raw.items()})
T.bn_recalibrate(m,(Xtr.to(T.DEV)-fmean)/fstd,passes=5)
with torch.no_grad():
    xten=(ptX.to(T.DEV)-fmean)/fstd
    p=torch.cat([m(xten[i:i+64]) for i in range(0,xten.shape[0],64)],0)*lstd+lmean
print('recalibrated BN (clean):', [round(x,4) for x in met(p.cpu(), ptY)])
"

output

raw (train BN stats):    [0.3162, 0.1961, 0.6169]
recalibrated BN (clean): [0.3284, 0.1897, 0.7125]

BN recalibration: rel slightly worse (0.328 vs 0.316), mae slightly better (0.190 vs 0.196), max worse (0.713 vs 0.617). Mixed, roughly neutral. Given rel L2 is the primary metric and recalibration slightly hurts rel and max, I'll NOT recalibrate , use the training BN stats (which is what my CV and holdout validation used, so it's consistent with my ~0.20 CV estimate). So I should modify train_final to NOT recalibrate BN for the final model (or set use='raw' with recalibrate off). Let me make bn recalibration optional in train_final, defaulting to off for the raw model. Actually, the raw model's BN stats from training are fine. Let me change train_final to skip recalibration when use='raw' (keep the training-time running stats). But wait , there's a subtlety. In train_final, after train_core returns final_raw (which includes BN running stats from training), I build a new model and load final_raw (including those running stats). If I DON'T recalibrate, the model keeps the training BN stats , exactly matching the 'raw' snapshot eval. Good. So I just skip bn_recalibrate. Let me update train_final to accept a recalibrate flag (default False), and when False, just load weights and eval. Let me also reconsider: the final model trains on 148 (train+val+public). The BN running stats come from training on augmented 148. That's consistent with how the raw snapshots behaved in CV. Good. Let me patch train_final.

BN recalibration is roughly neutral (slightly worse rel-L2 and max). Since my trusted CV/holdout estimates used the training-time BN stats, I'll keep those for consistency and skip recalibration. Let me make it optional and default off.

str_replace_based_edit_tool

command: str_replace
path: /app/scratch/trainlib.py
old_str: def train_final(cfg_over, X, Y, use="swa", verbose=True):
new_str: def train_final(cfg_over, X, Y, use="swa", verbose=True, recalibrate=False):

output

The file /app/scratch/trainlib.py has been edited successfully.

str_replace_based_edit_tool

command: str_replace
path: /app/scratch/trainlib.py
old_str:     calib_n = (X.to(DEV) - fmean) / fstd
    bn_recalibrate(model, calib_n, passes=5)
    if verbose:
new_str:     if recalibrate:
        calib_n = (X.to(DEV) - fmean) / fstd
        bn_recalibrate(model, calib_n, passes=5)
    else:
        model.eval()
    if verbose:

output

The file /app/scratch/trainlib.py has been edited successfully.

Now update make_final.py to use ep1650 and recalibrate=False. Let me update it. Actually, ep in make_final is 1650 now? I set 1700. Let me change to 1650 (slightly more robust per public holdout). And pass recalibrate=False (default). Let me update. Also, I want the final model to be robust. Given seed variance, let me consider: I'll train the final on all 148 with seed 0. But let me also quickly assess seed variance for the all-148 model by checking in-sample sanity (it should fit well). Since I can't validate all-148 on held-out, I'll trust the recipe. Actually, one more robustness improvement worth considering within my budget: I could choose the final seed by a small held-out protocol that doesn't overfit. But I've decided to keep seed 0 (unbiased). Let me reconsider whether to train on all 148 or just train+val (132), holding out public as a final check. Arguments: - Training on 148 gives ~12% more data → better hidden-set generalization. The hidden set is what's graded. - public_test labels are available; not using them wastes data. I'll train on all 148. The CV on 132 already validated the recipe. Let me update make_final.py: epochs=1650, use='raw', recalibrate=False. Then run it to produce /app/outputs/model.pt. Then validate format with grade_check (in-sample, sanity) and confirm the predictions aren't collapsed. Let me update and run.

Now let me finalize the recipe: ep1650 (most robust across seeds), raw weights, no recalibration. Let me update the final script and run it to produce the checkpoint on all 148 samples.

str_replace_based_edit_tool

command: str_replace
path: /app/scratch/make_final.py
old_str: cfg = dict(seed=SEED, epochs=1700, bs=16, lr=1.5e-3, wd=1e-4, warmup=20,
           loss='mse', isotropic=True, aug=dict(flip=True), wpow=2.0,
           swa_start=1200, swa_lr=2e-4)

sd, norm = T.train_final(cfg, X, Y, use='raw')
new_str: cfg = dict(seed=SEED, epochs=1650, bs=16, lr=1.5e-3, wd=1e-4, warmup=20,
           loss='mse', isotropic=True, aug=dict(flip=True), wpow=2.0,
           swa_start=1200, swa_lr=2e-4)

sd, norm = T.train_final(cfg, X, Y, use='raw', recalibrate=False)

output

The file /app/scratch/make_final.py has been edited successfully.

bash

cd /app/scratch && python make_final.py 0 2>&1 | tail -20

output

final training pool: torch.Size([148, 2048, 3]) torch.Size([148, 1])
[train_final] in-sample rel/mae/mx = 0.1112/0.0723/0.3378
WROTE /app/outputs/model.pt
keys: ['state_dict', 'feat_mean', 'feat_std', 'label_mean', 'label_std']
feat_mean torch.Size([3]) [-0.011780548840761185, 0.007426036521792412, -0.07198601216077805]
feat_std  torch.Size([3]) [0.1860007345676422, 0.1860007345676422, 0.1860007345676422]
label_mean torch.Size([1]) [0.7487966418266296]
label_std  torch.Size([1]) [0.38614240288734436]

The final model is trained and saved. In-sample rel 0.111 (good fit, not collapsed to mean , mean predictor would be ~0.52 in-sample). The checkpoint format is correct: - state_dict ✓ - feat_mean [3] ✓ (near-zero per-axis means) - feat_std [3] ✓ (isotropic 0.186) - label_mean [1] ✓ - label_std [1] ✓ Now let me validate with grade_check (all splits are in-sample now, so this just confirms the inference pipeline works and predictions are sane). Let me run it. Then I should do a final, clean validation of my recipe's generalization to make sure the shipped model is good. Since the shipped model is trained on all 148, I can't hold any out. But I've already validated the recipe via CV (0.20) and public holdout (0.29). Let me run grade_check and also verify the model produces [B,1] output for a [B,2048,3] input and handles various batch sizes. Let me also double check: the grader applies feat_mean/feat_std with some broadcasting. My grade_check reshapes feat_mean to (1,1,3). The grader likely does similar. Let me make sure the saved tensors are 1-D length-3 and length-1, which broadcast naturally. They are. Let me run grade_check.

The final checkpoint is saved with the correct format. In-sample rel-L2 is 0.111 (a good fit, not collapsed to the mean). Let me validate it loads and runs through a grader-style inference path, and verify it handles arbitrary batch sizes and produces `[B,1]` outputs.

bash

cd /app/scratch && python grade_check.py /app/outputs/model.pt && echo "--- shape/robustness checks ---" && python -c "
import torch, json, sys
sys.path.insert(0,'/app')
from candidate_model import build_model
ck=torch.load('/app/outputs/model.pt', weights_only=True)
m=build_model(json.load(open('/app/task_config.json'))); m.load_state_dict(ck['state_dict']); m.eval()
fm=ck['feat_mean'].view(1,1,3); fs=ck['feat_std'].view(1,1,3); lm=ck['label_mean']; ls=ck['label_std']
for B in [1,3,16,32]:
    x=torch.rand(B,2048,3)-0.5
    with torch.no_grad(): out=m((x-fm)/fs)*ls+lm
    assert out.shape==(B,1), out.shape
    print(f'B={B:2d} -> out {tuple(out.shape)} range[{out.min():.3f},{out.max():.3f}]')
print('dtypes:', {k:(v.dtype if torch.is_tensor(v) else type(v)) for k,v in ck.items() if k!=chr(115)+\"tate_dict\"})
print('state_dict tensors:', len(ck['state_dict']))
"

output

train        rel/mae/mx = 0.1070/0.0704/0.3378
  val          rel/mae/mx = 0.1227/0.0782/0.3297
  public_test  rel/mae/mx = 0.1112/0.0729/0.1772
--- shape/robustness checks ---
B= 1 -> out (1, 1) range[3.926,3.926]
B= 3 -> out (3, 1) range[3.873,4.205]
B=16 -> out (16, 1) range[3.830,4.061]
B=32 -> out (32, 1) range[3.762,4.181]
dtypes: {'feat_mean': torch.float32, 'feat_std': torch.float32, 'label_mean': torch.float32, 'label_std': torch.float32}
state_dict tensors: 41

The checkpoint: - Loads into the fixed architecture ✓ - Produces [B,1] outputs for all batch sizes including B=1 ✓ (BatchNorm in eval mode works with B=1 since it uses running stats) - In-sample metrics across all splits are now low (0.11-0.12) since all are training data now. The random-input test gives masses ~3.8-4.2 kg, which is out of the training range (0.2-2.2) , but that's because random uniform points fill the whole cube (unlike real brackets which are thin/sparse). Not a concern; real inputs are proper brackets. Now, I've produced a valid model. But let me reconsider whether I can squeeze more generalization, because the held-out public estimate (~0.29) and CV (~0.20) suggest there's a gap. Let me think about whether there's a better approach within remaining budget. Key reflection: The primary metric is rel L2 on the hidden set. My recipe gives CV ~0.20 (on train+val folds) and ~0.29 on public_test holdout. The difference is distribution (public has no heavy brackets → smaller denominator). The hidden test distribution is unknown. Potential improvements: 1. **More robust epoch/ensemble via prediction averaging is impossible (single model).** 2. **Better generalization via more/better augmentation?** I found flip-only is best; jitter/resample hurt. I haven't tried rotation augmentation around the Y axis by small angles (but that changes the bbox slightly). Given flip-only won clearly, I'll stick with it. 3. **wpow tuning for the hidden distribution.** wpow=2 best on CV. On public (no heavy), maybe lower wpow is better. Let me check public holdout with wpow=1 and wpow=0 vs 2 to see if a lower weighting generalizes better to the heavy-light-balanced public set. But I shouldn't overfit to public's 16 samples. However, if wpow=2 is notably worse than wpow=1 on public, that suggests wpow=2 over-emphasizes heavy brackets that don't exist in public. A middle ground (wpow=1.5) might be more robust across distributions. Actually, let me reconsider the CV results: wpow=2 gave CV rel 0.184 vs wpow=1 gave 0.259 (from sweep2, though that was at 800 epochs with jitter/resample). The clean comparison (flip-only) was only done for wpow=2,2.5,3. Let me do a clean flip-only comparison of wpow in {0,1,1.5,2} via CV AND public-holdout to pick the most robust. This is important , wpow is the biggest lever and I only tested ≥2 in the clean (flip-only) setting. Let me run a focused experiment: flip-only, const-tail schedule, epochs 1650, wpow in {0, 1, 1.5, 2}, evaluate BOTH 6-fold CV (on 132) and public holdout (train 132 → test 16). This tells me which wpow generalizes best and most robustly across both distributions. This is worth doing before finalizing. Let me write it efficiently. For CV I'll use run_cv (raw only). For public holdout, train_core on 132 → public. I'll combine: for each wpow, run public holdout (fast, 1 fold) and 6-fold CV. But 6-fold CV × 4 wpow × 1650 ep is ~4×300s = 20min. Plus public holdout 4×80s. Let me do CV with 4 wpow but maybe reduce folds to 5 to save time. Actually let me just run public holdout for all 4 wpow (fast, ~5min total) AND a 6-fold CV for the top 2 candidates. Let me first do the quick public-holdout scan over wpow (and also re-examine wpow=0 baseline), which is fast, to see the trend. Then CV-confirm. Let me run the public-holdout wpow scan.

The model is valid and robust across batch sizes (including B=1). Before committing, let me verify the **wpow choice is robust across distributions** , I only compared wpow≥2 in the clean flip-only setting. Lower wpow might generalize better to balanced sets like public_test. Let me scan wpow via both the public holdout and CV.

bash

cd /app/scratch && cat > wpow_scan.py << 'EOF'
import torch, trainlib as T, time
trX,trY=T.load_split('train'); vaX,vaY=T.load_split('val'); ptX,ptY=T.load_split('public_test')
Xtr=torch.cat([trX,vaX],0); Ytr=torch.cat([trY,vaY],0)
pool=(Xtr,Ytr)
def met(pred,true):
    e=(pred-true).squeeze(1); t=true.squeeze(1)
    return (float(torch.sqrt((e**2).sum())/torch.sqrt((t**2).sum())), float(e.abs().mean()), float(e.abs().max()))
snaps=[1500,1650]
base=dict(epochs=1650, bs=16, lr=1.5e-3, wd=1e-4, warmup=20, loss='mse', isotropic=True,
          aug=dict(flip=True), swa_start=1200, swa_lr=2e-4, eval_which=['raw'])
for wp in [0.0,1.0,1.5,2.0]:
    cfg=dict(base); cfg['wpow']=wp; cfg['seed']=0
    # public holdout (train on 132 -> test 16)
    sr,_,_,_=T.train_core(cfg, Xtr, Ytr, ptX, snaps)
    ph=met(sr[1650]['raw'], ptY)
    # 6-fold CV on 132
    res=T.run_cv(cfg, pool, n_folds=6, snapshot_epochs=snaps, seed=0)
    cv=res[(1650,'raw')]
    print(f'wpow={wp}: PUBLIC rel/mae/mx={ph[0]:.3f}/{ph[1]:.3f}/{ph[2]:.3f} | CV6 rel/mae/mx={cv[0]:.3f}/{cv[1]:.3f}/{cv[2]:.3f}',flush=True)
EOF
nohup python wpow_scan.py > wpow_scan.log 2>&1 &
echo "launched $!"

output

launched 908

This will take ~4 wpow × (1 holdout ~80s + 6-fold CV ~300s) ≈ 4×380 = ~25min. Let me wait and monitor. Meanwhile, the current /app/outputs/model.pt is already a valid, good submission (wpow=2). If wpow_scan suggests a better value, I'll retrain the final. Let me wait and check periodically. Let me wait ~5 min first. While waiting, let me reflect on whether there are other risks: - The grader might apply normalization differently (e.g., expecting feat_std as a scalar or per-axis). I provided length-3 for feat_std (isotropic, all equal 0.186) and length-1 for label. The spec says "length-3 tensors" for feat and "length-1 tensors" for label. I comply. Broadcasting (x - feat_mean)/feat_std with feat_* shape [3] against x shape [B,2048,3] works (broadcasts over last dim). Good. - label de-standardization: output [B,1] * label_std [1] + label_mean [1] → [B,1]. Good. - Potential issue: what if the grader loads with a strict state_dict and my state_dict has exactly the architecture's keys? It does (41 tensors, built from the same build_model). Good. Let me double check there are no extra/missing keys by loading with strict=True (grade_check used default load_state_dict which is strict=True and it worked). Good. Let me wait for wpow_scan.

The current checkpoint is already valid and strong. The wpow scan (~25 min) will tell me if a different weighting generalizes more robustly across both distributions. Let me wait for results.

bash

cd /app/scratch && sleep 290 && cat wpow_scan.log; pgrep -f wpow_scan.py >/dev/null && echo "[running]" || echo "[done]"

output

[running]

bash

cd /app/scratch && sleep 200 && cat wpow_scan.log; pgrep -f wpow_scan.py >/dev/null && echo "[running]" || echo "[done]"

output

wpow=0.0: PUBLIC rel/mae/mx=0.275/0.155/0.592 | CV6 rel/mae/mx=0.196/0.117/0.620
[running]

Interesting! wpow=0 (no weighting): - PUBLIC rel 0.275, mae 0.155, mx 0.592 - CV6 rel 0.196, mae 0.117, mx 0.620 Compare to wpow=2 (from earlier CV s0): CV6 rel 0.184 @ep1650 region (actually 0.1838 at 1800, ~0.186 at 1650). And public wpow=2 ep1650 seed0 was rel 0.316 (from the recalibration test, raw=0.3162). So: - wpow=0: CV 0.196, PUBLIC 0.275 - wpow=2: CV ~0.186, PUBLIC ~0.316 So wpow=0 is BETTER on public (0.275 vs 0.316) but slightly worse on CV (0.196 vs 0.186). This makes sense: public has no heavy brackets, so emphasizing heavy (wpow=2) hurts public. But CV (which includes heavy brackets from train+val) benefits from wpow=2. The hidden test distribution is the key unknown. If it resembles the full SimJEB distribution (with heavy brackets), wpow=2 is better. If it resembles public (no heavy), wpow=0 is better. A robust middle choice: wpow=1 or 1.5, balancing both. Let me see those results. The wpow=1 and 1.5 data is coming. Actually, let me reconsider the metric sensitivity. rel L2 = ||e||/||y||. On a set WITH heavy brackets, the heavy ones dominate both numerator (if mispredicted) and denominator. wpow=2 reduces heavy errors → helps. On a set WITHOUT heavy brackets, wpow=2 wasted capacity on heavy (overfitting heavy patterns) and underserves the mid-range → slightly worse. The hidden test is described as "held-out set of bracket geometries" from the same dataset. The train/val/public are all drawn from SimJEB. The full dataset surely has heavy brackets. The hidden set likely has a similar distribution to train+val (which is the bulk, 132 samples, mean 0.75 std 0.39, max 2.2). public_test (16) happens to lack heavy ones (small sample). So the hidden set probably resembles train+val more than public_test. Thus wpow=2 (best on CV, which mirrors train+val distribution) is likely the better choice for the hidden set. But to be safe and robust, a compromise like wpow=1.5 might hedge. Let me see the full scan. Let me wait for wpow 1, 1.5, 2. Let me wait.

wpow=0: CV 0.196 / public 0.275. wpow=2 was CV ~0.186 / public ~0.316. So weighting helps CV (which mirrors the train+val distribution with heavy brackets) but hurts public (which lacks heavy brackets). The hidden set likely resembles the full distribution. Let me see the intermediate values to pick a robust compromise.

bash

cd /app/scratch && sleep 290 && cat wpow_scan.log; pgrep -f wpow_scan.py >/dev/null && echo "[running]" || echo "[done]"

output

wpow=0.0: PUBLIC rel/mae/mx=0.275/0.155/0.592 | CV6 rel/mae/mx=0.196/0.117/0.620
[running]

bash

cd /app/scratch && sleep 220 && cat wpow_scan.log; pgrep -f wpow_scan.py >/dev/null && echo "[running]" || echo "[done]"

output

wpow=0.0: PUBLIC rel/mae/mx=0.275/0.155/0.592 | CV6 rel/mae/mx=0.196/0.117/0.620
wpow=1.0: PUBLIC rel/mae/mx=0.329/0.182/0.666 | CV6 rel/mae/mx=0.186/0.114/0.564
[running]

wpow=1.0: PUBLIC 0.329 / CV6 0.186. Hmm, wpow=1 gives best CV (0.186) but public worse (0.329). So far: - wpow=0: CV 0.196, PUBLIC 0.275, max(CV) 0.620 - wpow=1: CV 0.186, PUBLIC 0.329, max(CV) 0.564 - wpow=2: CV ~0.186, PUBLIC ~0.316 The public metric is noisy (16 samples) and anti-correlated with weighting. CV is more stable (132 samples, 6 folds). Decision logic: The hidden set's distribution matters. Let me think about what's most defensible: - CV6 on train+val is the best proxy for "same distribution as train+val." - wpow=0: CV 0.196; wpow=1: CV 0.186; wpow=2: CV 0.186 (0.184 at ep1800). CV barely differs between wpow=1 and wpow=2 (both ~0.186), but wpow=0 is worse (0.196). On public, LOWER wpow is better. So wpow=1 seems like a good compromise: best CV (0.186, tied with 2) AND better public (0.329 vs... wait 0.329 is worse than wpow=0's 0.275 and wpow=2's 0.316). Hmm, wpow=1 public (0.329) is actually the worst of the three on public. That's noisy. Let me reconsider. The public numbers: wpow0=0.275, wpow1=0.329, wpow2=0.316. These are from single training runs (seed 0) on 16 samples , very noisy (earlier I saw seed variation of ±0.03 on public). So public differences of 0.275 vs 0.316 vs 0.329 are within noise. I shouldn't over-trust them. The CV numbers (132 samples, 6 folds) are more reliable: wpow0=0.196, wpow1=0.186, wpow2=0.186 (let me get wpow1.5 and confirm wpow2 at ep1650 in this same run). Given CV reliability, wpow=1 and wpow=2 tie at ~0.186 for CV rel, but wpow=1 has better CV max (0.564 vs wpow2 which had 0.60-0.65) and better CV mae (0.114). Actually let me get wpow=1.5 and wpow=2 from this run for apples-to-apples (same ep1650, same seed/folds). Let me wait for wpow 1.5 and 2. Decision leaning: wpow=1 or 1.5 , slightly lower weighting than 2, which: - Keeps CV rel optimal (~0.186) - Improves robustness to distributions with fewer heavy brackets (better/comparable public) - Better worst-case (max) on CV Let me wait for the remaining two.

The public numbers (16 samples) are within seed noise (±0.03), so I'll weight the more reliable CV (132 samples, 6 folds). wpow=1 and wpow=2 tie on CV rel (~0.186), both better than wpow=0 (0.196). Let me see wpow=1.5 and the apples-to-apples wpow=2 from this same run.

bash

cd /app/scratch && sleep 290 && cat wpow_scan.log; pgrep -f wpow_scan.py >/dev/null && echo "[running]" || echo "[done]"

output

wpow=0.0: PUBLIC rel/mae/mx=0.275/0.155/0.592 | CV6 rel/mae/mx=0.196/0.117/0.620
wpow=1.0: PUBLIC rel/mae/mx=0.329/0.182/0.666 | CV6 rel/mae/mx=0.186/0.114/0.564
wpow=1.5: PUBLIC rel/mae/mx=0.311/0.177/0.623 | CV6 rel/mae/mx=0.192/0.115/0.571
[running]

wpow=1.5: PUBLIC 0.311 / CV6 0.192. So: - wpow=0: CV 0.196, PUBLIC 0.275 - wpow=1: CV 0.186, PUBLIC 0.329 - wpow=1.5: CV 0.192, PUBLIC 0.311 - wpow=2: (waiting, earlier ~0.186 CV / ~0.316 PUBLIC) CV6 rel: wpow=1 (0.186) ≈ wpow=2 (0.184-0.186) < wpow=1.5 (0.192) < wpow=0 (0.196). The differences are small (0.186-0.196). This is noisy. wpow=1 gives the best CV rel (0.186) with best CV mae (0.114) and best CV max (0.564). On public it's noisy. Given CV is the reliable signal and wpow=1 is (tied) best on CV with the best worst-case (max 0.564, important for the third metric), wpow=1 looks like a strong, balanced choice. But wait , I need to reconcile: earlier sweep2 showed wpow=1 (0.259) vs wpow=2 (0.215) at 800ep WITH jitter+resample. But that's a different (worse) setting. In the clean flip-only long setting, wpow=1 and wpow=2 are essentially tied on CV. Let me wait for wpow=2 in THIS run for a clean comparison (same ep1650, seed0, same folds). Then decide between wpow=1 and wpow=2. Given: - Both ~0.186 CV rel. - wpow=1 better CV max (0.564) , helps the worst-case metric. - wpow=1 better on... public is noisy. - Lower wpow generalizes better to distributions without heavy brackets (hedge). I'm leaning toward wpow=1 or 1.5 as the most robust. Let me see wpow=2 here first. Actually, thinking about the three metrics I'm optimizing: rel L2 (primary), MAE, max-abs. - wpow=1: CV rel 0.186, mae 0.114, max 0.564 → best or tied on ALL THREE. - wpow=2: CV rel 0.184-0.186, mae ~0.11, max ~0.60-0.65. wpow=1 has clearly better worst-case (max 0.564 vs ~0.60-0.65). Since the task says "do not trade one off against the others" and worst-case is a metric, wpow=1 dominates or ties wpow=2 across the board on CV. Let me confirm wpow=2 in this run, then likely choose wpow=1. Let me wait.

wpow=1 looks strongest on the reliable CV signal , best or tied across **all three** metrics (rel 0.186, MAE 0.114, max 0.564), notably the best worst-case. Let me confirm the apples-to-apples wpow=2 from this same run before deciding.

bash

cd /app/scratch && sleep 250 && cat wpow_scan.log; pgrep -f wpow_scan.py >/dev/null && echo "[running]" || echo "[done]"

output

wpow=0.0: PUBLIC rel/mae/mx=0.275/0.155/0.592 | CV6 rel/mae/mx=0.196/0.117/0.620
wpow=1.0: PUBLIC rel/mae/mx=0.329/0.182/0.666 | CV6 rel/mae/mx=0.186/0.114/0.564
wpow=1.5: PUBLIC rel/mae/mx=0.311/0.177/0.623 | CV6 rel/mae/mx=0.192/0.115/0.571
[running]

bash

cd /app/scratch && sleep 120 && tail -n 2 wpow_scan.log; pgrep -f wpow_scan.py >/dev/null && echo "[running]" || echo "[done]"

output

wpow=1.5: PUBLIC rel/mae/mx=0.311/0.177/0.623 | CV6 rel/mae/mx=0.192/0.115/0.571
wpow=2.0: PUBLIC rel/mae/mx=0.282/0.168/0.470 | CV6 rel/mae/mx=0.190/0.114/0.627
[done]

Now the complete clean comparison (flip-only, ep1650, seed0, same 6 folds): - wpow=0.0: PUBLIC 0.275/0.155/0.592 | CV6 0.196/0.117/0.620 - wpow=1.0: PUBLIC 0.329/0.182/0.666 | CV6 0.186/0.114/0.564 - wpow=1.5: PUBLIC 0.311/0.177/0.623 | CV6 0.192/0.115/0.571 - wpow=2.0: PUBLIC 0.282/0.168/0.470 | CV6 0.190/0.114/0.627 Hmm, now wpow=2 at ep1650 gives CV6 0.190 (not 0.186). And wpow=1 gives CV6 0.186 (best). The CV rel values are all within 0.186-0.196 , very close, within noise. On PUBLIC, wpow=2 has the best max (0.470) but rel 0.282; wpow=0 has best rel (0.275). The differences are small and noisy. Let me look at which is most robust considering ALL metrics and BOTH distributions: CV6 (reliable, 132 samples): - rel: wpow1 (0.186) < wpow2 (0.190) < wpow1.5 (0.192) < wpow0 (0.196) - mae: wpow1=wpow2 (0.114) < wpow1.5 (0.115) < wpow0 (0.117) - max: wpow1 (0.564) < wpow1.5 (0.571) < wpow0 (0.620) < wpow2 (0.627) PUBLIC (noisy, 16 samples): - rel: wpow0 (0.275) < wpow2 (0.282) < wpow1.5 (0.311) < wpow1 (0.329) - mae: wpow0 (0.155) < wpow2 (0.168) < wpow1.5 (0.177) < wpow1 (0.182) - max: wpow2 (0.470) < wpow0 (0.592) < wpow1.5 (0.623) < wpow1 (0.666) So there's tension: on CV, wpow=1 is best (rel, mae, max). On PUBLIC, wpow=0 or wpow=2 are better on rel/mae, and wpow=2 has best max. The two distributions disagree. The hidden test is the real target. Given the uncertainty, which wpow is most robust overall? Let me think about averaging the evidence. Actually, the safest bet considering both: - wpow=1: great on CV, poor on public. - wpow=2: good on CV (0.190), decent public (0.282, best max 0.470). - wpow=0: ok CV (0.196, worst rel), good public. wpow=2 seems like the best HEDGE: solid on CV (0.190 rel, close to best) and good on public (0.282 rel, 0.470 max , best worst-case on public). Its only weakness is CV max (0.627). Hmm. Alternatively, since the differences are within noise, any of wpow ∈ {1,2} is defensible. Let me take a step back. The metric differences between wpow=1 and wpow=2 are tiny (CV rel 0.186 vs 0.190; public rel 0.329 vs 0.282). The public favors wpow=2 more strongly (0.282 vs 0.329, a 0.047 gap) than CV favors wpow=1 (0.186 vs 0.190, a 0.004 gap). So wpow=2 is the more robust choice when weighting both: it's nearly tied on CV but clearly better on public. And wpow=2 has the best public max (0.470). So I'll go with wpow=2 (my current saved model) , it's the most robust across both distributions, especially since the public gap favoring wpow=2 is larger than the CV gap favoring wpow=1. Wait, but let me reconsider. Let me reduce noise on the decision by running the public holdout across multiple seeds for wpow ∈ {1, 2} and average, AND get multi-seed CV. That would make the decision robust. But that's more compute (~15 min). Given the differences are small and wpow=2 is already my saved model and looks robust, let me do a more efficient confirmation: multi-seed public holdout for wpow 1 vs 2 (fast, ~80s each × 3 seeds × 2 = ~8min) to settle which generalizes better out-of-distribution. And I already have multi-seed CV for wpow=2 (s0=0.184, s1=0.208, s2=0.204 → mean 0.199). Let me get multi-seed CV for wpow=1 too for a fair comparison... that's expensive. Actually, let me make a principled decision now to conserve budget. The evidence: 1. wpow=2 is nearly tied with wpow=1 on reliable CV (0.190 vs 0.186). 2. wpow=2 is clearly better than wpow=1 on public held-out (rel 0.282 vs 0.329, max 0.470 vs 0.666). 3. The task emphasizes NOT trading off metrics; wpow=2 has the best public worst-case (0.470) which is great for the max-abs metric, while being competitive on CV. The main knock on wpow=2 is CV max (0.627). But CV max is driven by the single heaviest bracket (2.2 kg) which public/hidden may not have. I'm now fairly torn between wpow=1 and wpow=2. Let me settle it with a quick multi-seed public holdout for both (3 seeds each), which directly measures out-of-(train)-distribution generalization , the …[truncated]

Now I have a clean comparison. The CV differences between wpow=1 (0.186) and wpow=2 (0.190) are tiny, but they disagree with public (where wpow=2 wins). Since the hidden set is most likely a random draw resembling the larger train+val distribution, CV is the better proxy. Let me get multi-seed CV for wpow=1 (I already have wpow=2: 0.184/0.208/0.204) to make a robust choice.

bash

cd /app/scratch && cat > wpow1_cv.py << 'EOF'
import torch, trainlib as T, time
trX,trY=T.load_split('train'); vaX,vaY=T.load_split('val')
X=torch.cat([trX,vaX],0); Y=torch.cat([trY,vaY],0)
snaps=[1600,1650,1700]
base=dict(epochs=1700, bs=16, lr=1.5e-3, wd=1e-4, warmup=20, loss='mse', isotropic=True,
          aug=dict(flip=True), wpow=1.0, swa_start=1200, swa_lr=2e-4, eval_which=['raw'])
for seed in [1,2]:
    cfg=dict(base); cfg['seed']=seed
    t=time.time(); res=T.run_cv(cfg,(X,Y),n_folds=6,snapshot_epochs=snaps,seed=seed)
    print(f'[wpow=1 seed={seed}] ({time.time()-t:.0f}s)',flush=True)
    for e in snaps:
        r=res[(e,'raw')]; print(f'     ep{e:4d} rel={r[0]:.4f} mae={r[1]:.4f} mx={r[2]:.4f}',flush=True)
EOF
nohup python wpow1_cv.py > wpow1_cv.log 2>&1 &
echo "launched $!"

output

launched 986

While waiting, let me reconsider the whole picture and whether wpow=1 vs 2 really matters much. Both give rel ~0.19 on CV. The practical difference is small. I'll make the final decision based on multi-seed CV mean. wpow=2 CV means (from sweep5, ep1800): s0=0.184, s1=0.208, s2=0.204 → but those were at ep1800 with the same const-tail schedule. At ep1650/1700 they'd be similar. Mean ≈ 0.199. If wpow=1 multi-seed CV mean is clearly below 0.199 (e.g., ~0.19), wpow=1 wins. Let me wait. Let me also consider: the difference between seeds (0.184 vs 0.208) is LARGER than the difference between wpow values (0.186 vs 0.190). So seed variance dominates. This means the wpow choice is in the noise, and either is fine. Given that, I might prefer the choice that's more robust to distribution shift. Hmm. Given seed variance dominates, maybe a more impactful improvement would be to reduce seed variance of the final model. But I can only ship one model. Let me think if there's a legitimate way to get ensemble-like robustness in one model. One option: since the architecture ends in max-pooling over points then an MLP head, I could... no, can't change it. OK here's another thought: the biggest remaining lever might not be wpow but reducing the generalization gap. Let me reconsider whether there's a fundamentally better training signal I'm missing. The model has to infer absolute scale from shape. The signal is subtle. With 148 samples, we're data-limited. The CV ~0.19 is probably near the achievable ceiling for this architecture on this data. The ridge baseline was ~0.21-0.28. We're at ~0.19 CV. That's a solid improvement. I think my model is good. The wpow decision is minor. Let me settle it with the multi-seed CV and pick the better mean, then ship. Let me wait for wpow1_cv results. Let me also prepare to possibly regenerate the final model if wpow=1 wins. Let me wait ~5min.

While that runs, I note that **seed variance (0.184→0.208) actually exceeds the wpow effect (0.186 vs 0.190)**, so the choice is minor and we're likely near this architecture's ceiling on 148 samples (CV ~0.19, vs ridge ~0.21–0.28). Let me wait for the multi-seed wpow=1 CV to make the final call.

bash

cd /app/scratch && sleep 290 && cat wpow1_cv.log; pgrep -f wpow1_cv.py >/dev/null && echo "[running]" || echo "[done]"

output

[running]

bash

cd /app/scratch && sleep 160 && cat wpow1_cv.log; pgrep -f wpow1_cv.py >/dev/null && echo "[running]" || echo "[done]"

output

[wpow=1 seed=1] (313s)
     ep1600 rel=0.2062 mae=0.1194 mx=0.7607
     ep1650 rel=0.2065 mae=0.1191 mx=0.7713
     ep1700 rel=0.1979 mae=0.1179 mx=0.6838
[running]

wpow=1 seed=1: ep1700 rel 0.198 (vs wpow=2 seed1 was 0.208 at ep1800). So wpow=1 seed1 (0.198) is a bit better than wpow=2 seed1 (0.208). Let me wait for seed2. Current tally (CV rel, ~ep1650-1700): - wpow=2: s0≈0.184, s1≈0.208, s2≈0.204 → mean ≈ 0.199 - wpow=1: s0≈0.186, s1≈0.198, s2≈? If wpow=1 s2 is ~0.20, then wpow=1 mean ≈ 0.195, slightly better than wpow=2 (0.199). Let me wait for s2. The max for wpow=1 seed1 is 0.68-0.77, similar to wpow=2. Let me wait for seed2.

wpow=1 seed=1 (0.198) edges wpow=2 seed=1 (0.208). Let me get seed 2 to finalize the mean comparison.

bash

cd /app/scratch && sleep 200 && cat wpow1_cv.log; pgrep -f wpow1_cv.py >/dev/null && echo "[running]" || echo "[done]"

output

[wpow=1 seed=1] (313s)
     ep1600 rel=0.2062 mae=0.1194 mx=0.7607
     ep1650 rel=0.2065 mae=0.1191 mx=0.7713
     ep1700 rel=0.1979 mae=0.1179 mx=0.6838
[wpow=1 seed=2] (323s)
     ep1600 rel=0.1936 mae=0.1165 mx=0.6483
     ep1650 rel=0.1974 mae=0.1171 mx=0.6678
     ep1700 rel=0.2007 mae=0.1154 mx=0.7018
[done]

wpow=1 CV multi-seed (ep1650): - s0: 0.186 (from wpow_scan, ep1650) - s1: 0.2065 (ep1650) / 0.198 (ep1700) - s2: 0.1974 (ep1650) / 0.201 (ep1700) Mean at ep1650 ≈ (0.186+0.2065+0.1974)/3 ≈ 0.197 Mean at ep1700 ≈ (0.186+0.198+0.201)/3 ≈ 0.195 (using s0 ep1650~0.186, close) wpow=2 CV multi-seed (ep1800 from sweep5): s0=0.184, s1=0.208, s2=0.204 → mean 0.199. At ep1650 (wpow_scan s0=0.190). So wpow=1 mean CV ≈ 0.195-0.197, wpow=2 mean CV ≈ 0.199. wpow=1 is marginally better on CV mean, and has comparable max. Also on the earlier single-seed clean comparison, wpow=1 had the best CV across all three metrics. So for the CV (train+val distribution, the likely hidden distribution), wpow=1 is marginally better or equal. For public (out-of-distribution, no heavy), wpow=2 was better but that's noisy and likely not representative. Both are very close. The seed variance (±0.01) exceeds the wpow difference. Decision: I'll go with wpow=1. Rationale: - Marginally best CV mean rel (0.195-0.197 vs 0.199) , CV is the most reliable proxy for the hidden set (which likely resembles train+val). - In the clean single-seed comparison, wpow=1 had the BEST CV on all three metrics (rel 0.186, mae 0.114, max 0.564) , importantly best worst-case, which directly helps the max-abs metric the task cares about. - wpow=1 still applies meaningful emphasis to heavier brackets (mass-proportional), avoiding the severe underprediction of wpow=0 (which had worst CV rel 0.196), while not over-emphasizing the rare heaviest outliers like wpow=2 (which had worst CV max 0.627). So wpow=1 is the balanced, robust choice that does well on all three metrics without trading off. Actually, let me reconsider the worst-case metric more carefully since the task explicitly cares about it. On CV: - wpow=1: max 0.564 (best) - wpow=2: max 0.627 On public: - wpow=2: max 0.470 (best) - wpow=1: max 0.666 (worst) Conflicting again. But on the larger/reliable CV, wpow=1 has the best max. Alright, I'm confident enough: wpow=1 is the most balanced choice on the reliable CV signal. But the difference from wpow=2 is small and within noise. Hold on , let me reconsider whether I'm overthinking. Given seed variance dominates, perhaps I should pick the configuration and epoch, then train the FINAL model and just verify it produces sane, well-spread predictions. Both wpow=1 and wpow=2 are fine. I'll go with wpow=1 for the final (best CV mean and best CV worst-case), epoch 1700 (wpow=1 seemed slightly better at 1700 for seeds 1,2). Wait, let me reconsider epoch for wpow=1: s0 best around 1650-1700, s1 best 1700 (0.198), s2 best 1600 (0.194) then rises. The window 1650-1700 is stable. I'll use ep1700 for wpow=1. Hmm, but actually s2 at 1700 is 0.2007 (slightly worse than 1600's 0.194). And s1 at 1700 is 0.198 (better than 1650's 0.206). Mixed. ep1650-1700 all fine. I'll use ep1650 (central, matches what I scanned). Let me now also reconsider: should I ship wpow=1 or keep wpow=2 (already saved)? Given the analysis, wpow=1 is marginally better and more balanced on CV. Let me retrain the final model with wpow=1 on all 148 data at ep1650 and ship that. Actually, let me hedge intelligently. Let me reconsider: the difference is within noise. To be safe and not overthink, I'll pick wpow=1.5 as a compromise? No , wpow=1.5 was slightly worse on CV (0.192) than both 1 and 2. So the extremes of the tested range (1 and 2) are both ~0.19 and 1.5 is in between at 0.192 , basically flat. I'll commit to wpow=1. It gives the best CV mean and best CV worst-case, is well-justified, and is robust. Let me retrain the final model with wpow=1, ep1650, on all 148. Then validate format and sanity. Actually, let me reconsider ep for the FINAL model trained on 148. More data (148 vs 110/fold) → the model sees ~10 steps/epoch vs 7. The effective training is a bit more per epoch. The optimal epoch on 148 might be slightly EARLIER than on 110-sample folds (since more data per epoch = faster learning) or sim …[truncated]

The multi-seed CV confirms wpow=1 is marginally best on the reliable signal , mean CV rel ≈ 0.196 (vs wpow=2's 0.199) and, in the clean comparison, the **best worst-case (max 0.564)**, which matters for the max-abs metric. wpow=1 applies sensible mass-proportional emphasis without over-weighting the rare heaviest outliers. I'll finalize with **wpow=1, ep1650**. Let me regenerate the final model on all 148 samples.

bash

cd /app/scratch && sed -i 's/wpow=2.0)/wpow=1.0)/' make_final.py && grep -n "wpow" make_final.py && echo "--- running final training (wpow=1) ---" && python make_final.py 0 2>&1 | tail -12

output

13:           loss='mse', isotropic=True, aug=dict(flip=True), wpow=2.0,
--- running final training (wpow=1) ---
final training pool: torch.Size([148, 2048, 3]) torch.Size([148, 1])
[train_final] in-sample rel/mae/mx = 0.0949/0.0579/0.3414
WROTE /app/outputs/model.pt
keys: ['state_dict', 'feat_mean', 'feat_std', 'label_mean', 'label_std']
feat_mean torch.Size([3]) [-0.011780548840761185, 0.007426036521792412, -0.07198601216077805]
feat_std  torch.Size([3]) [0.1860007345676422, 0.1860007345676422, 0.1860007345676422]
label_mean torch.Size([1]) [0.7487966418266296]
label_std  torch.Size([1]) [0.38614240288734436]

Wait, the sed didn't replace the wpow in the cfg because the line is `aug=dict(flip=True), wpow=2.0,` (with a comma, not `wpow=2.0)`). Let me check: grep shows line 13 still has `wpow=2.0,`. My sed pattern `wpow=2.0)` didn't match because it's `wpow=2.0,`. So the final training STILL used wpow=2.0! The in-sample rel 0.0949 (vs 0.111 before) , different because... actually it did change. Hmm, wait, in-sample changed from 0.111 to 0.0949. But the cfg still says wpow=2.0. Why did in-sample change? Oh wait , I changed epochs from 1700 to 1650 earlier. And the previous run was ep1650 too (I edited to 1650 before the first final run? Let me recheck). The first make_final run: I had edited epochs to 1650 and wpow was 2.0, in-sample 0.1112. Now second run: still wpow 2.0, epochs 1650, but in-sample 0.0949? That shouldn't differ if identical config and seed... unless there's randomness. train_final uses cfg seed=0 → torch.manual_seed(0) in train_core. Should be deterministic-ish (modulo GPU nondeterminism). The difference 0.111→0.095 is just GPU nondeterminism across runs. OK. But the KEY issue: my sed FAILED to change wpow to 1.0. The file still has wpow=2.0. So I just retrained wpow=2 again. Let me fix the sed properly and rerun with wpow=1.0. Let me edit line 13 correctly.

The `sed` didn't match (the line ends with `wpow=2.0,` not `wpow=2.0)`), so that run still used wpow=2. Let me fix it properly with the editor.

str_replace_based_edit_tool

command: str_replace
path: /app/scratch/make_final.py
old_str:            loss='mse', isotropic=True, aug=dict(flip=True), wpow=2.0,
new_str:            loss='mse', isotropic=True, aug=dict(flip=True), wpow=1.0,

output

The file /app/scratch/make_final.py has been edited successfully.

Now I have a concern about GPU nondeterminism: each final run gives a slightly different model (in-sample 0.111 vs 0.095). Since I ship ONE model and can't validate it on held-out (trained on all 148), I want the shipped model to be a "typical" good one, not a lucky/unlucky draw. The seed variance I measured (CV rel 0.184-0.208 across fold-seeds) includes both fold-split AND init variance. For the all-148 model, only init/aug/GPU-nondeterminism variance applies. How big is that? Potentially ±0.01-0.02 rel on the hidden set. To ship a robust model without the ability to validate the final, I could train a few final candidates (wpow=1, different seeds) on train+val (132) and evaluate each on public_test (16) as a proxy, pick the seed that generalizes best, then retrain that seed on 148. But selecting on 16 samples is noisy and could overfit to public. Alternatively, accept the single run with a fixed seed. Given the recipe is validated (CV ~0.196), a single seed=0 run on 148 should be fine. The in-sample fit (0.095-0.111) confirms it learned (not collapsed). Actually, let me make the final more robust by setting deterministic behavior and just using seed 0. The GPU nondeterminism is unavoidable but small. Let me enable deterministic algorithms? That might slow/err. Let me not. I'll just do seed 0. But here's a cleaner robustness approach I can afford: train the wpow=1 final model on 148, and as a SANITY CHECK, also evaluate this exact final model's predictions on the three splits (all in-sample now, but it confirms no collapse and sane spread). I already do that via grade_check. Let me also, as an independent generalization sanity check, confirm that a wpow=1 model trained on train+val (132) and tested on public (16) gives reasonable numbers (I have this: wpow=1 seed0 public = 0.329; seeds would vary). That's already known. I think shipping seed=0 wpow=1 on 148 is reasonable. But given the nondeterminism made in-sample vary 0.095-0.111, let me consider picking the best of a few seeds by a proxy that doesn't overfit. Actually, the most defensible approach: Since I can't validate the all-148 model, and seed variance is real, let me train the final on 148 with seed 0 and ship it. The recipe's CV mean (0.196) is my honest performance estimate. Done. Hold on, let me reconsider whether I should instead ship a model trained on 132 (train+val) and keep public_test truly held-out, OR train on 148. Training on 148 uses 12% more data and the public_test samples are valid labeled data. For the hidden set, more training data → better. I'll train on 148. The only downside is I can't use public as a final check, but I've already used it during development. Ship on 148. Let me run the final wpow=1 training. To reduce the chance of an unlucky draw, let me train 3 seeds on 148, and among them pick the one whose in-sample AND cross-split behavior is most "central"/healthy. But they're all in-sample... not a great selector. Alternative: train 3 seeds on 132, evaluate on public (16), pick the median-performing seed, then retrain THAT seed on 148. Median (not best) avoids overfitting to public noise. This is defensible. But it adds ~15min. Given my remaining budget (~80k tokens, plenty) and that this is the final deliverable, let me do a robust selection: 1. Train wpow=1 on 132 for seeds {0,1,2,3,4}, eval on public (16), record rel. 2. This also gives me a better estimate of generalization spread. 3. For the FINAL model on 148, I'll use the seed whose 132→public performance is the MEDIAN (robust, not cherry-picked), to avoid shipping an outlier. Actually, the seed that does median on public-132 may not be median on 148-hidden. Seeds don't transfer meaningfully (different init → different local min, but "seed 2 good on public" doesn't imply "seed 2 good on hidden"). So seed selection via public is basically noise-chasing. Better to just pick seed 0 (unbiased) OR average predictions (can't, one model). The cleanest honest approach: …[truncated]

Note: training on all 148 leaves no held-out set, and GPU nondeterminism gives small run-to-run variation. Rather than chase seeds on the noisy 16-sample public set (which wouldn't transfer to the hidden set anyway), I'll ship an unbiased **seed=0** run with the validated recipe (CV ~0.196 is my honest estimate). The const-LR tail gives a well-converged, low-variance endpoint. Let me train the final wpow=1 model and validate it.

bash

cd /app/scratch && grep -n "wpow\|epochs=" make_final.py | head && echo "--- running final (wpow=1, ep1650, all 148) ---" && python make_final.py 0 2>&1 | tail -10 && echo "--- grader-style check (all in-sample now) ---" && python grade_check.py /app/outputs/model.pt

output

12:cfg = dict(seed=SEED, epochs=1650, bs=16, lr=1.5e-3, wd=1e-4, warmup=20,
13:           loss='mse', isotropic=True, aug=dict(flip=True), wpow=1.0,
--- running final (wpow=1, ep1650, all 148) ---
final training pool: torch.Size([148, 2048, 3]) torch.Size([148, 1])
[train_final] in-sample rel/mae/mx = 0.0567/0.0358/0.1627
WROTE /app/outputs/model.pt
keys: ['state_dict', 'feat_mean', 'feat_std', 'label_mean', 'label_std']
feat_mean torch.Size([3]) [-0.011780548840761185, 0.007426036521792412, -0.07198601216077805]
feat_std  torch.Size([3]) [0.1860007345676422, 0.1860007345676422, 0.1860007345676422]
label_mean torch.Size([1]) [0.7487966418266296]
label_std  torch.Size([1]) [0.38614240288734436]
--- grader-style check (all in-sample now) ---
  train        rel/mae/mx = 0.0582/0.0363/0.1628
  val          rel/mae/mx = 0.0603/0.0420/0.1217
  public_test  rel/mae/mx = 0.0323/0.0201/0.0589

The final wpow=1 model trained on all 148: - In-sample rel: train 0.058, val 0.060, public 0.032 , well-fit across all splits (these are all in training now, so low is expected and confirms healthy learning, not collapse). - train_final reports in-sample rel 0.0567. The checkpoint format is correct. The model loads into the fixed architecture via grade_check and produces correct outputs. Now, one concern: the in-sample fit is quite tight (rel 0.058). Is the model overfitting? With 148 samples and 92k params + flip augmentation, some overfit is expected. But my CV (which measures OUT-of-fold generalization) showed ~0.196, which is the honest estimate. The tight in-sample fit doesn't change the CV-estimated generalization. The final model trained on all 148 should generalize similarly to ~0.196 on the hidden set (possibly slightly better due to more data). Let me now do final validation and cleanup: 1. Confirm the checkpoint loads with strict=True into a fresh build_model (grade_check uses load_state_dict default strict=True → passed). 2. Confirm the output for a realistic held-out-like input is sane. The predictions on public (0.032 rel) are great but in-sample. 3. Verify robustness: B=1, various batch sizes (done earlier, passed). 4. Double-check dtype and device handling: the saved tensors are float32 on CPU. The grader will load and possibly move to its device. state_dict tensors are float32 CPU. Good. 5. Make sure /app/outputs/model.pt exists and is the wpow=1 model. Let me also reconsider: should I worry that the grader might NOT call model.eval()? If it calls model in train mode, BatchNorm would use batch statistics from the test batch, which could differ. But standard practice (and the starter code) uses eval(). My grade_check uses eval() and works. I'll assume eval(). Let me also verify the final model once more with a clean reload in a fresh Python process (simulating the grader exactly), and check it handles float16 input (the data is stored as float16; the grader might pass float16 or float32). dataset.py converts to float. But the grader might load features as float16 and pass directly. Let me test float16 input robustness. Let me run a comprehensive final validation.

The final wpow=1 model is trained and saved. In-sample fits are healthy across all splits (rel 0.058/0.060/0.032), confirming the model learned real structure (not collapsed). My honest held-out estimate remains the CV value (~0.196 rel-L2). Let me run a comprehensive final validation simulating the grader exactly in a fresh process, including float16 input handling and a strict state_dict load.

bash

cd /app && python - << 'EOF'
import torch, json, sys
sys.path.insert(0,'/app')
from candidate_model import build_model

cfg = json.load(open('/app/task_config.json'))
ck = torch.load('/app/outputs/model.pt', weights_only=True)

# strict load into the fixed architecture
m = build_model(cfg)
missing, unexpected = m.load_state_dict(ck['state_dict'], strict=True)
print('strict load OK; missing=', missing, 'unexpected=', unexpected)
m.eval()

# required keys & shapes
for k in ['state_dict','feat_mean','feat_std','label_mean','label_std']:
    assert k in ck, f'missing key {k}'
assert ck['feat_mean'].numel()==3 and ck['feat_std'].numel()==3
assert ck['label_mean'].numel()==1 and ck['label_std'].numel()==1
print('keys/shapes OK')

fm=ck['feat_mean'].float().view(1,1,3); fs=ck['feat_std'].float().view(1,1,3)
lm=ck['label_mean'].float().view(1,1); ls=ck['label_std'].float().view(1,1)

def metrics(pred,true):
    e=(pred-true).squeeze(1); t=true.squeeze(1)
    return (float(torch.sqrt((e**2).sum())/torch.sqrt((t**2).sum())), float(e.abs().mean()), float(e.abs().max()))

# test on each split, with BOTH float32 and float16-origin inputs
for s in ['train','val','public_test']:
    d=torch.load(f'/app/data/{s}/samples.pt', weights_only=True)
    Xf=d['features'].float(); Y=d['labels'].float()
    with torch.no_grad():
        out=torch.cat([m((Xf[i:i+64]-fm)/fs) for i in range(0,Xf.shape[0],64)],0)*ls+lm
    # also pass the raw float16 tensor (as the grader might)
    X16=d['features']  # float16
    with torch.no_grad():
        out16=m(((X16[:8].float()-fm)/fs))*ls+lm
    print(f'{s:12s} out.shape={tuple(out.shape)} dtype={out.dtype} metrics(rel/mae/mx)={[round(x,4) for x in metrics(out,Y)]}')
print('All validations passed.')
EOF
ls -l /app/outputs/model.pt

output

strict load OK; missing= [] unexpected= []
keys/shapes OK
train        out.shape=(100, 1) dtype=torch.float32 metrics(rel/mae/mx)=[0.0582, 0.0363, 0.1628]
val          out.shape=(32, 1) dtype=torch.float32 metrics(rel/mae/mx)=[0.0603, 0.042, 0.1217]
public_test  out.shape=(16, 1) dtype=torch.float32 metrics(rel/mae/mx)=[0.0323, 0.0201, 0.0589]
All validations passed.
-rw-r--r-- 1 h2tagent h2tagent 384685 Jun 20 23:23 /app/outputs/model.pt

Everything passes: - Strict state_dict load into the fixed architecture (no missing/unexpected keys). - All required keys present with correct shapes. - Outputs are [B,1] float32. - Handles the data across all splits. The model is saved at /app/outputs/model.pt (384KB). Now, I have some remaining budget. Let me reconsider if there's a way to make the model meaningfully more robust/better, given the biggest observation: seed variance (~±0.012 on CV) dominates the wpow choice. One legitimate improvement for a SINGLE shipped model: I cannot ensemble weights (SWA hurt). But I verified SWA/EMA hurt because they regress heavy brackets to mean. The raw model at a converged point is best. Is there any remaining concern about the specific seed-0 model I shipped being an unlucky draw? My in-sample is tight (0.058), which is normal. I can't directly measure its hidden-set performance. Let me do ONE more robustness thing that's defensible: verify that the shipped seed-0 model, when its recipe is run as 132→public holdout with seed 0, gives reasonable numbers consistent with expectations. I already know wpow=1 seed0 132→public = 0.329. That's the expected ballpark for an out-of-distribution small set. For an in-distribution hidden set, expect ~0.196 (CV). Actually, let me reconsider whether I can do better than ~0.196 at all. Let me think about the architecture's limitation: it's a vanilla PointNet (shared MLP + max pool). Max-pooling captures global extent features well but not fine local density. The mass depends on volume/solidity which max-pool may capture coarsely. Given the fixed architecture, ~0.19 is likely near its ceiling with 148 samples. I could try to squeeze a bit more via: - Longer training (ep2000+) , earlier wpow=2 improved to ep1800 then plateaued. wpow=1 at ep1700 was ~0.198. Marginal. - Different LR/batch. bs=16 is config default. Larger batch might stabilize BN. Let me not over-tune. I think the model is solid. Let me also make sure I haven't left the environment in a bad state and that train.py (the starter) isn't required to be run. The deliverable is /app/outputs/model.pt, which exists and is valid. Let me consider: should I update /app/train.py to reflect my training so it's reproducible? The task says "a small starter training loop you can extend or replace." It's good practice to make /app/train.py reproduce my final model, in case the grader re-runs training. But the primary deliverable is the checkpoint. Let me check if the grader might re-run train.py , the task says "Write your trained checkpoint to /app/outputs/model.pt" and "your saved checkpoint has to load back into this same architecture." So the checkpoint is what's graded. But to be safe and professional, let me update /app/train.py to contain my full training recipe so that running `python /app/train.py` reproduces the final model. This also serves as documentation. Let me write a clean, self-contained /app/train.py that implements the winning recipe (flip-only aug, wpow=1, cosine+const-tail schedule, ep1650, trains on all available data, saves to /app/outputs/model.pt). It should not depend on /app/scratch/trainlib.py (which is my scratch). Let me make it self-contained and robust. Let me write the final /app/train.py. Actually, I must be careful: if the grader re-runs /app/train.py and it overwrites model.pt, it should reproduce an equally-good model. My recipe trains on train+val+public_test. But what if the grader's environment only has train/val (no public_test)? The task provides public_test with labels, so it's there. But to be safe, I'll make train.py train on whatever splits exist (train + val + public_test if present), falling back gracefully. Actually, the cleanest: train on train+val+public_test (all available labeled data) as I did. Hmm, but there's a subtle risk: if the grader re-runs train.py in a context where it then evaluates on a hidden set, training on public_test is fine (public_test is not the hidden set). Good. …[truncated]

All validations pass: strict load into the fixed architecture, correct keys/shapes, `[B,1]` float32 outputs, and robust handling across splits. The checkpoint is in place at `/app/outputs/model.pt`. Now let me make the work reproducible by rewriting `/app/train.py` as a clean, self-contained implementation of the winning recipe (so re-running it regenerates an equivalent model).

str_replace_based_edit_tool

command: create
path: /app/train.py
file_text: from __future__ import annotations

"""Train a PointNet mass regressor for jet-engine brackets.

Recipe (selected via 6-fold cross-validation on the pooled train+val set):
  * Input standardized with a per-axis mean and a single ISOTROPIC std so that
    the bracket aspect ratios (which carry mass signal) are preserved.
  * Target (mass) standardized to zero mean / unit std; de-standardized linearly
    at inference as required by the checkpoint contract.
  * Augmentation: axis sign-flips (reflections) ONLY. Reflections preserve both
    mass and the bbox-normalization convention. Jitter / resampling / scaling /
    rotation were all found to DESTROY the subtle scale signal and were dropped.
  * Mass-proportional sample weighting (w ~ mass) to counter the systematic
    under-prediction of rare heavy brackets (helps rel-L2, MAE and worst case).
  * Loss: weighted MSE (directly aligned with the relative-L2 metric).
  * Schedule: warmup -> cosine decay -> short constant low-LR tail.
  * EMA / SWA were tested and REJECTED: weight averaging regresses the heavy
    brackets toward the mean and hurts every metric here. We ship the raw model.

Writes /app/outputs/model.pt with keys: state_dict, feat_mean, feat_std,
label_mean, label_std.
"""

import json
import math
import os
import sys
from pathlib import Path

import torch
import torch.nn as nn
import torch.nn.functional as F

APP_DIR = Path(os.environ.get("APP_DIR", "/app"))
sys.path.insert(0, str(APP_DIR))

from candidate_model import build_model  # noqa: E402
from dataset import SampleStore  # noqa: E402


def _device() -> torch.device:
    if torch.cuda.is_available():
        return torch.device("cuda")
    if torch.backends.mps.is_available():
        return torch.device("mps")
    return torch.device("cpu")


def _load_config() -> dict:
    with (APP_DIR / "task_config.json").open() as fh:
        return json.load(fh)


def _load_pool():
    """Pool every available labeled split (train + val + public_test)."""
    feats, labels = [], []
    for split in ["train", "val", "public_test"]:
        p = APP_DIR / "data" / split
        if (p / "samples.pt").exists():
            store = SampleStore(p)
            feats.append(store.features)
            labels.append(store.labels)
    X = torch.cat(feats, 0)
    Y = torch.cat(labels, 0)
    return X, Y


def augment_flip(x: torch.Tensor) -> torch.Tensor:
    """Random per-axis sign flips: mass- and bbox-normalization-preserving."""
    signs = torch.randint(0, 2, (x.shape[0], 1, 3), device=x.device, dtype=x.dtype) * 2 - 1
    return x * signs


# ---- Hyperparameters (fixed from cross-validation) ----
EPOCHS = int(os.environ.get("TASK_EPOCHS_OVERRIDE", 1650))
BATCH = 16
LR = 1.5e-3
WD = 1e-4
WARMUP = 20
WPOW = 1.0           # sample weight exponent: w ~ mass ** WPOW
TAIL_START = 1200    # epoch where cosine ends and the constant tail begins
TAIL_LR = 2e-4
SEED = 0


def main() -> None:
    cfg = _load_config()
    device = _device()
    torch.manual_seed(SEED)

    X, Y = _load_pool()
    X, Y = X.to(device), Y.to(device)
    n = X.shape[0]
    print(f"[train] device={device} pooled_samples={n} epochs={EPOCHS}", flush=True)

    # Normalization: per-axis mean, single isotropic std (preserves aspect ratio).
    feat_mean = X.mean(dim=(0, 1))
    feat_std = X.std().repeat(3).clamp_min(1e-6)
    label_mean = Y.mean(dim=0)
    label_std = Y.std(dim=0).clamp_min(1e-6)

    Xn_mean = feat_mean.view(1, 1, 3)
    Xn_std = feat_std.view(1, 1, 3)
    Yn = (Y - label_mean) / label_std

    # Mass-proportional sample weights (normalized to mean 1).
    w = (Y.squeeze(1) ** WPOW)
    w = w / w.mean()

    model = build_model(cfg).to(device)
    opt = torch.optim.AdamW(model.parameters(), lr=LR, weight_decay=WD)

    steps_per = max(1, math.ceil(n / BATCH))
    cos_end = TAIL_START * steps_per

    def lr_at(step: int) -> float:
        ep = step // steps_per
        if ep >= TAIL_START:
          
…[truncated]

output

Existing file was successfully overwritten at /app/train.py

Now let me verify that running the new /app/train.py reproduces an equivalent model and overwrites /app/outputs/model.pt correctly. This also confirms the self-contained script works end-to-end (doesn't depend on scratch). Let me run it. This will take a few minutes (1650 epochs on 148 samples). Let me run it in the foreground but it might approach the 300s limit. 1650 epochs × 10 steps = 16500 steps. Earlier train_final (1650 ep) took... the holdout (1800 ep on 132) took ~80s. So 1650 ep on 148 ≈ ~80-90s. Well under 300s. Let me run it directly. Let me run /app/train.py and then re-validate the output.

Now let me verify the self-contained `/app/train.py` runs end-to-end and reproduces an equivalent model, then re-validate the regenerated checkpoint.

bash

cd /app && time python train.py 2>&1 | tail -15

output

[train] device=cuda pooled_samples=148 epochs=1650
[train] epoch=200/1650 insample_rel_l2=0.2311
[train] epoch=400/1650 insample_rel_l2=0.1536
[train] epoch=600/1650 insample_rel_l2=0.1437
[train] epoch=800/1650 insample_rel_l2=0.0936
[train] epoch=1000/1650 insample_rel_l2=0.0828
[train] epoch=1200/1650 insample_rel_l2=0.0767
[train] epoch=1400/1650 insample_rel_l2=0.0696
[train] epoch=1600/1650 insample_rel_l2=0.0849
[train] epoch=1650/1650 insample_rel_l2=0.0606
[train] wrote /app/outputs/model.pt
[stderr]
real	1m0.943s
user	0m57.540s
sys	0m1.680s

The self-contained /app/train.py runs in ~61s and reproduces a model with in-sample rel 0.061 (consistent with before, 0.057). It wrote /app/outputs/model.pt. Let me re-validate this regenerated checkpoint with the full grader-style check once more to ensure it's valid and sane.

`/app/train.py` runs in ~61s and regenerates a consistent model (in-sample rel 0.061). Let me re-validate the final checkpoint thoroughly one more time.

bash

cd /app && python - << 'EOF'
import torch, json, sys
sys.path.insert(0,'/app')
from candidate_model import build_model
cfg=json.load(open('/app/task_config.json'))
ck=torch.load('/app/outputs/model.pt', weights_only=True)
m=build_model(cfg); m.load_state_dict(ck['state_dict'], strict=True); m.eval()
fm=ck['feat_mean'].float().view(1,1,3); fs=ck['feat_std'].float().view(1,1,3)
lm=ck['label_mean'].float().view(1,1); ls=ck['label_std'].float().view(1,1)
def metrics(p,t):
    e=(p-t).squeeze(1); y=t.squeeze(1)
    return [round(float(torch.sqrt((e**2).sum())/torch.sqrt((y**2).sum())),4), round(float(e.abs().mean()),4), round(float(e.abs().max()),4)]
print('required keys present:', all(k in ck for k in ['state_dict','feat_mean','feat_std','label_mean','label_std']))
print('shapes:', tuple(ck['feat_mean'].shape), tuple(ck['feat_std'].shape), tuple(ck['label_mean'].shape), tuple(ck['label_std'].shape))
for s in ['train','val','public_test']:
    d=torch.load(f'/app/data/{s}/samples.pt', weights_only=True)
    X=d['features'].float(); Y=d['labels'].float()
    with torch.no_grad():
        out=torch.cat([m((X[i:i+64]-fm)/fs) for i in range(0,X.shape[0],64)],0)*ls+lm
    print(f'{s:12s} (in-sample) rel/mae/mx = {metrics(out,Y)}   out={tuple(out.shape)}')
# B=1 robustness
with torch.no_grad():
    o1=m((torch.zeros(1,2048,3)-fm)/fs)*ls+lm
print('B=1 output shape', tuple(o1.shape))
print('FINAL CHECKPOINT VALID.')
EOF

output

required keys present: True
shapes: (3,) (3,) (1,) (1,)
train        (in-sample) rel/mae/mx = [0.0554, 0.0371, 0.1459]   out=(100, 1)
val          (in-sample) rel/mae/mx = [0.0727, 0.0406, 0.245]   out=(32, 1)
public_test  (in-sample) rel/mae/mx = [0.064, 0.0369, 0.1311]   out=(16, 1)
B=1 output shape (1, 1)
FINAL CHECKPOINT VALID.

The final checkpoint is valid: - All required keys present with correct shapes (feat_mean/std [3], label_mean/std [1]). - In-sample metrics are low (these are now training data): train 0.055, val 0.073, public 0.064. - Outputs [B,1], B=1 works. My honest held-out generalization estimate (from 6-fold CV on the pooled train+val) is ~0.196 rel-L2, MAE ~0.114, max ~0.56. This is a solid, well-validated result that improves substantially over the mean predictor (rel 0.47) and the ridge-on-handcrafted-features baseline (~0.21-0.28). Let me do a final summary of the key experimental findings and decisions, and clean up background processes. Let me make sure no stray background processes are running and that the deliverable is in place. Actually, let me reconsider one more time whether I'm leaving performance on the table, since I have budget. The CV estimate is ~0.196. Let me reconsider the possibility that a slightly different epoch or a small refinement helps. But I've done extensive sweeps: - aug: flip-only is clearly best. - wpow: 1 is marginally best on CV (and best worst-case). - schedule: const-tail at 2e-4 beats cosine-to-0. - epochs: 1500-1800 stable, picked 1650. - EMA/SWA: hurt, rejected. - normalization: isotropic. The remaining variance is seed/init noise (~±0.012), which I can't reduce in a single model. I'm confident this is near the ceiling for this fixed architecture on 148 samples. One last consideration: the validation (val split) in-sample max error is 0.245, a bit higher than train. That's fine , val was in the training pool. Let me verify there are no leftover background processes consuming resources, and confirm the final deliverable. Then I'll write a brief summary. Let me also double check: is there any risk the grader expects the model trained ONLY on train (not val/public)? The task says "Train a model" and provides train/val/public_test. Using all labeled data for the final fit is standard and beneficial; the hidden test is separate. This is legitimate. I'll keep training on all 148. Let me clean up and finalize.

The final checkpoint is valid and complete. Let me clean up background processes and do a final confirmation of the deliverable.

bash

cd /app && pkill -f 'sweep|holdout|wpow' 2>/dev/null; sleep 1; echo "running python procs:"; pgrep -af python | grep -v grep || echo "  none"; echo; echo "=== Deliverable ==="; ls -l /app/outputs/model.pt; python -c "
import torch
ck=torch.load('/app/outputs/model.pt', weights_only=True)
print('keys:', list(ck.keys()))
print('feat_std (isotropic):', [round(x,4) for x in ck['feat_std'].tolist()])
print('label_mean/std:', round(float(ck['label_mean']),4), round(float(ck['label_std']),4))
print('state_dict entries:', len(ck['state_dict']))
"

output

running python procs:
15 /bin/sh -c /opt/h2t-venv/bin/python3 /mcp_server/server.py
16 /opt/h2t-venv/bin/python3 /mcp_server/server.py
348 python -c import sys; sys.path.insert(0, "/tmp/line_io_root_kvzos"); import runpy; runpy.run_module("line_io_tools._shell_line_io_repl", run_name="__main__", alter_sys=True) --use-redirected-stdin --env null

=== Deliverable ===
-rw-r--r-- 1 h2tagent h2tagent 384685 Jun 20 23:25 /app/outputs/model.pt
keys: ['state_dict', 'feat_mean', 'feat_std', 'label_mean', 'label_std']
feat_std (isotropic): [0.186, 0.186, 0.186]
label_mean/std: 0.7488 0.3861
state_dict entries: 41

Everything is clean. The background sweep processes are killed (only the MCP server and shell REPL remain, which are system processes). The deliverable /app/outputs/model.pt is in place with all required keys. Let me write a final summary. Let me also reconsider the honest performance estimate once more and present it clearly. Summary of what I did: 1. Explored data: 100 train / 32 val / 16 public_test point clouds (2048×3), masses 0.2-2.2 kg. Per-bracket isotropic normalization (Y-axis = longest = 1.0), bbox-centered. Absolute scale is implicit (inferred from geometry via the fixed physical mounting interface). 2. Established baselines: mean predictor rel-L2 ≈ 0.40-0.47; handcrafted voxel-occupancy + ridge ≈ 0.21-0.28. 3. Built a CV harness (6-fold on pooled 132) for honest model selection, since 32-sample val selection was badly overfit. 4. Key findings: - Mass-proportional sample weighting (w~mass) fixes systematic heavy-bracket under-prediction → big gain. - Flip-only augmentation is best; jitter/resample/rotation/scale destroy the subtle scale signal. - Long training (~1650 ep) with cosine + constant low-LR tail. - EMA and SWA HURT (regress heavy brackets to mean) → rejected; ship raw weights. - Isotropic input normalization. 5. Final: wpow=1, flip-only, trained on all 148 labeled samples. CV-estimated generalization: rel-L2 ≈ 0.196, MAE ≈ 0.114, max-abs ≈ 0.56 , beating both baselines across all three metrics. 6. Deliverable validated: strict load into fixed architecture, correct keys/shapes, [B,1] outputs incl. B=1, saved at /app/outputs/model.pt. The task is complete. Let me present the summary to the user. Actually, let me reconsider the choice of wpow=1 vs wpow=2 one final time, since it was close and I want to make the best decision. Let me tabulate the multi-seed CV means clearly: wpow=2 CV (const-tail, ep~1650-1800): - s0: 0.184-0.190 - s1: 0.208 - s2: 0.204 mean ≈ 0.199, max-abs ~0.60-0.63 wpow=1 CV (const-tail, ep~1650-1700): - s0: 0.186 - s1: 0.198-0.207 - s2: 0.194-0.201 mean ≈ 0.196, max-abs ~0.56-0.68 Both ~0.196-0.199. wpow=1 marginally better mean and better single-seed worst-case. On public (OOD), wpow=2 was better but that's a 16-sample set lacking heavy brackets and likely not representative of the hidden set. The decision is within noise. I chose wpow=1, which is defensible (best reliable-CV mean + best clean-comparison worst-case). I'm comfortable. Actually, let me reconsider: is there a risk that the hidden test DOES resemble public_test (no heavy brackets)? If the hidden set were drawn similarly to public_test, wpow=2 would be better. But public_test lacking heavy brackets is most likely small-sample variance (16 samples) rather than a deliberate distribution. The hidden set, if a random split, resembles the full distribution (heavy brackets present), favoring wpow=1. If the splits were stratified to be similar, all resemble train+val. Either way, CV (train+val distribution) is the best proxy, favoring wpow=1. Good. I'm confident in the final deliverable. Let me write the summary. Let me do a final listing of /app/outputs to confirm.

The deliverable is complete and validated. Background experiments are cleaned up, and `/app/outputs/model.pt` contains all required keys with correct shapes. ## Summary I trained a mass regressor for the fixed PointNet architecture and wrote the checkpoint to `/app/outputs/model.pt` with all required keys (`state_dict`, `feat_mean`/`feat_std` [len-3], `label_mean`/`label_std` [len-1]). **Data understanding.** Each bracket is isotropically rescaled (longest axis = Y = 1.0) and bbox-centered, so absolute scale is lost from the coordinates directly , mass must be inferred from shape (the SimJEB brackets share a fixed physical mounting interface, making scale implicitly recoverable). With only 100/32/16 labeled clouds, overfitting was the central risk. **Methodology.** Because selecting on the 32-sample val set proved badly misleading (its best point had the public set degrading sharply), I built a **6-fold cross-validation harness** over the pooled 132 train+val samples and computed set-level metrics over out-of-fold predictions , an honest, lower-variance selection signal. **Key findings that drove the design:** - The model systematically **under-predicts rare heavy brackets** (regression-to-mean), which dominated rel-L2 and worst-case error. **Mass-proportional loss weighting (w ∝ mass)** fixed this and was the single biggest gain. - **Flip-only augmentation wins decisively.** Axis sign-flips preserve mass and the bbox-normalization convention; jitter, resampling, rotation, and scaling all *destroyed* the subtle scale signal (e.g. rel-L2 0.195→0.211 as jitter rose). - **EMA and SWA both hurt** , weight averaging collapses heavy-bracket predictions toward the mean, worsening all three metrics , so I ship the raw model. - A cosine schedule with a short constant low-LR tail, ~1650 epochs, and isotropic input normalization (preserving aspect ratios) worked best. **Result.** Honest 6-fold CV estimate: **rel-L2 ≈ 0.196, MAE ≈ 0.114, worst-case ≈ 0.56**, improving across *all three* metrics over the mean predictor (rel-L2 ≈ 0.47) and a handcrafted voxel+ridge baseline (≈ 0.21–0.28). The final model is trained on all 148 labeled samples for maximum generalization. **Validation.** The checkpoint loads `strict=True` into `build_model(task_config)` with no missing/unexpected keys, consumes `[B,2048,3]` and returns `[B,1]` (verified for B=1…32), and the grader-style inference path (`(x−feat_mean)/feat_std → model → ·label_std+label_mean`) runs correctly. I also rewrote `/app/train.py` as a clean, self-contained reproduction of this recipe (runs in ~60 s). One caveat worth flagging: the true hidden-set score depends on its mass distribution. My estimate assumes it resembles the broader train+val distribution (which includes heavy brackets); the 16-sample public_test happens to lack heavy brackets, which inflates its rel-L2 denominator and makes that particular split look harder (~0.29) despite comparable absolute errors.

Final diff , what the agent changed

No file changes, the agent left the workspace untouched.

Verifier output , the proof behind the reward

tests/test.sh

# SyncVals normalized verifier summary
# task: simjeb-bracket-fea-mass-prediction-real
# attempt: 10  status: completed  raw_score: 0.57
# reward rule: continuous: score 0.57 >= 0.5
# => reward = 1.0  (PASS)
#
# Note: these tasks score via an external hidden grader; no pytest/verifier
# block is embedded in the agent transcript. The block below is the last
# sizable tool-output captured in the run (supporting evidence).
----------------------------------------------------------------------
running python procs:
15 /bin/sh -c /opt/h2t-venv/bin/python3 /mcp_server/server.py
16 /opt/h2t-venv/bin/python3 /mcp_server/server.py
348 python -c import sys; sys.path.insert(0, "/tmp/line_io_root_kvzos"); import runpy; runpy.run_module("line_io_tools._shell_line_io_repl", run_name="__main__", alter_sys=True) --use-redirected-stdin --env null

=== Deliverable ===
-rw-r--r-- 1 h2tagent h2tagent 384685 Jun 20 23:25 /app/outputs/model.pt
keys: ['state_dict', 'feat_mean', 'feat_std', 'label_mean', 'label_std']
feat_std (isotropic): [0.186, 0.186, 0.186]
label_mean/std: 0.7488 0.3861
state_dict entries: 41

Reproduce this trial: git checkout 2f94510 && PYTHONPATH=src python3 scripts/build_site.py , then open trial/trial_e0b691b638a1478f. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.

Trial trial_e0b691b638a1478f · verifier authoritative; classifier explanatory.