SyncVals · Trajectory

ks-equation-1d-forecast

claude-code claude-opus-4-8 ✗ failed GOOD_FAILURE ↑ 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_FAILUREHonest miss, the agent ran correctly but couldn't solve it. Expected for a hard task; the task is sound.

SubtypeOptimization/Hyperparameter Tuning

EvidenceThe agent reported metrics on val/public_test (rel-L2: 0.0023 and 0.0016) that appear excellent and 4.4-7.3x better than baseline. However, the hidden grader reported raw_score: 0.44 (below 0.5 threshold for passing). This indicates the agent's selected hyperparameters (EPOCHS=80, LR=2e-3, curriculum ending at R=16) did not generalize to the hidden test distribution. The agent's contract compliance was perfect: model.pt loads correctly with weights_only=True, state_dict matches build_model exactly (no missing/unexpected keys), feat_mean/feat_std present as floats. The agent implemented multi-step BPTT training with curriculum (1→2→4→6→8→10→12→16), EMA, relative-L2 loss, and KS symmetry augmentation, theoretically sound but failed to achieve passing performance on hidden grader metrics.

Root causeThe agent's hyperparameter selection (80 epochs, specific curriculum ramp, EMA decay 0.999, learning rate 2e-3) was optimized against the visible val/public_test splits but did not generalize to the hidden test set used by the grader. Hidden test metrics appear to have been substantially worse than the agent's local evaluation predicted, resulting in a score of 0.44 (between baseline and threshold) rather than the target 0.5+.

RecommendationN/A - task is fine

Trajectory

Tool-by-tool agent trajectory

164 tool calls · 3 tool types · 164 steps

# Kuramoto-Sivashinsky 1D Forecasting Train a neural surrogate to forecast the 1D Kuramoto-Sivashinsky equation: u_t + u*u_x + u_xx + u_xxxx = 0 on x in [0, L] periodic, L=22 This equation produces spatiotemporal chaos , small errors compound quickly over the rollout. ## What You Predict Given the last k=4 frames as input window [B, 4, 128], predict the next single frame [B, 1, 128]. At evaluation the hidden grader rolls your model autoregressively for 20 steps and scores the full rollout. ## Files Available - `/app/data/train/trajectories.pt` , 200 trajectories, shape [200, 100, 128] - `/app/data/val/trajectories.pt` , 32 trajectories - `/app/data/public_test/trajectories.pt` , 16 trajectories - `/app/task_config.json` , model config, training defaults - `/app/feature_schema.json` , grid spec and channel definitions - `/app/candidate_model.py` , fixed FNO-1D architecture - `/app/dataset.py` , KSForecast dataset yielding (window, next_frame) pairs - `/app/train.py` , weak baseline training script ## What To Do Improve the training recipe. You may change loss, optimizer, scheduler, augmentation, EMA, epochs. The checkpoint must be compatible with build_model(config) in candidate_model.py. Save your checkpoint to `/app/outputs/model.pt`. ## Output Contract model.pt must be a dict loadable with torch.load(..., weights_only=True) containing: - state_dict , state dict matching build_model(task_config) - feat_mean, feat_std , float scalars for input/output standardization ## Local Training ```bash python3 /app/train.py TASK_EPOCHS_OVERRIDE=2 python3 /app/train.py ```

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 architecture I need to work with.

bash

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

output

total 15
drwxrws--- 3 h2tagent h2tagent  138 Jun 19 18:00 .
drwxr-xr-x 1 root     root     4096 Jun 20 05:50 ..
-rw-rw---- 1 h2tagent h2tagent 2710 Jun 19 17:59 candidate_model.py
drwxrws--- 5 h2tagent h2tagent   58 Jun 19 17:59 data
-rw-rw---- 1 h2tagent h2tagent 1195 Jun 19 17:59 dataset.py
-rw-rw---- 1 h2tagent h2tagent  238 Jun 19 17:59 feature_schema.json
-rw-rw---- 1 h2tagent h2tagent  633 Jun 19 17:59 task_config.json
-rw-rw---- 1 h2tagent h2tagent 3184 Jun 19 17:59 train.py
---CONFIG---
{
  "task_id": "ks-equation-1d-forecast",
  "dataset_name": "ks-equation-1d-chaotic",
  "feature_format": "trajectory-window-k-frames",
  "label_format": "next-frame",
  "grid_size": 128,
  "in_channels": 4,
  "out_channels": 1,
  "rollout_horizon": 20,
  "seed": 20260517,
  "model": {
    "width": 32,
    "modes": 16,
    "num_layers": 4
  },
  "training": {
    "epochs": 10,
    "batch_size": 64,
    "lr": 0.001,
    "weight_decay": 0.0001,
    "loss": "MSE"
  },
  "evaluation": {
    "primary_metric": "rollout_rel_l2_score",
    "secondary_metrics": [
      "rollout_rmse_score",
      "final_step_rel_l2_score"
    ]
  }
}
---SCHEMA---
{
  "domain": "1d-periodic",
  "grid_size": 128,
  "domain_length": 22.0,
  "dt_effective": 0.25,
  "input": {"channels": 4, "description": "last k=4 frames of u(x,t)"},
  "output": {"channels": 1, "description": "next frame u(x,t+1)"}
}

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

Let me examine the model architecture, dataset, and the baseline 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 SpectralConv1d(nn.Module):
    def __init__(self, in_channels: int, out_channels: int, modes: int) -> None:
        super().__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.modes = modes
        scale = 1.0 / (in_channels * out_channels)
        self.weight = nn.Parameter(
            scale * torch.randn(in_channels, out_channels, modes, dtype=torch.cfloat)
        )

    @staticmethod
    def _mul1d(inp: torch.Tensor, weight: torch.Tensor) -> torch.Tensor:
        return torch.einsum("bix,iox->box", inp, weight)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        b, _, n = x.shape
        x_ft = torch.fft.rfft(x, norm="ortho")
        out_ft = torch.zeros(b, self.out_channels, n // 2 + 1, dtype=torch.cfloat, device=x.device)
        m = min(self.modes, x_ft.shape[-1])
        out_ft[:, :, :m] = self._mul1d(x_ft[:, :, :m], self.weight[:, :, :m])
        return torch.fft.irfft(out_ft, n=n, norm="ortho")


class FNO1dBlock(nn.Module):
    def __init__(self, width: int, modes: int) -> None:
        super().__init__()
        self.spectral = SpectralConv1d(width, width, modes)
        self.bypass = nn.Conv1d(width, width, 1)
        self.act = nn.GELU()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.act(self.spectral(x) + self.bypass(x))


class FNO1d(nn.Module):
    def __init__(self, in_channels: int, out_channels: int, width: int, modes: int, num_layers: int, grid_size: int) -> None:
        super().__init__()
        self.lift = nn.Conv1d(in_channels + 1, width, 1)
        self.blocks = nn.ModuleList(FNO1dBlock(width, modes) for _ in range(num_layers))
        self.proj = nn.Sequential(
            nn.Conv1d(width, width, 1),
            nn.GELU(),
            nn.Conv1d(width, out_channels, 1),
        )
        self.register_buffer("_grid", torch.linspace(0.0, 1.0, grid_size + 1)[:grid_size].view(1, 1, grid_size), persistent=False)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        b = x.shape[0]
        grid = self._grid.expand(b, -1, -1).to(x.device, dtype=x.dtype)
        x = torch.cat([x, grid], dim=1)
        x = self.lift(x)
        for block in self.blocks:
            x = block(x)
        return self.proj(x)


def build_model(config: Mapping) -> FNO1d:
    m = config["model"]
    return FNO1d(
        in_channels=int(config["in_channels"]),
        out_channels=int(config["out_channels"]),
        width=int(m["width"]),
        modes=int(m["modes"]),
        num_layers=int(m["num_layers"]),
        grid_size=int(config["grid_size"]),
    )
===dataset.py===
from __future__ import annotations
from pathlib import Path
import torch
from torch.utils.data import Dataset


class KSForecast(Dataset):
    def __init__(self, split_dir: str | Path, k: int = 4) -> None:
        self.dir = Path(split_dir)
        self.k = int(k)
        payload = torch.load(self.dir / "trajectories.pt", map_location="cpu", weights_only=True)
        self.trajectories: torch.Tensor = payload["trajectories"].float()
        n_traj, t_stored, _ = self.trajectories.shape
        if t_stored <= self.k:
            raise ValueError(f"trajectories too short for k={self.k}: t_stored={t_stored}")
        self._index = [(i, t) for i in range(n_traj) for t in range(self.k, t_stored)]

    def __len__(self) -> int:
        return len(self._index)

    def __getitem__(self, idx: int) -> dict:
        i, t = self._index[idx]
        return {
            "window": self.trajectories[i, t - self.k : t],
            "next_frame": self.trajectories[i, t : t + 1],
        }


def compute_standardization(trajectories: torch.Tensor) -> tuple[float, float]:
    mean = float(trajectories.mean().item())
    std = float(trajectories.std().clamp_min(1e-6).item())
    return mean, std
===train.py===
from __future__ import annotations
imp
…[truncated]

Now let me understand the data and compute environment. Let me check: 1. The data shapes and statistics 2. Whether there's a GPU 3. The PyTorch version

Now let me inspect the compute environment and the actual data characteristics.

bash

cd /app && python3 -c "
import torch, json
print('torch version:', torch.__version__)
print('cuda available:', torch.cuda.is_available())
if torch.cuda.is_available():
    print('device:', torch.cuda.get_device_name(0))
import os
print('CPU count:', os.cpu_count())
" && echo "---MEM---" && free -h && echo "---NPROC---" && nproc

output

torch version: 2.5.1+cu124
cuda available: True
device: NVIDIA H100 80GB HBM3
CPU count: 3
---MEM---
               total        used        free      shared  buff/cache   available
Mem:            30Gi        63Mi        29Gi          0B        29Mi        29Gi
Swap:             0B          0B          0B
---NPROC---
3

I have an H100 GPU available. Let me examine the data characteristics in detail.

bash

cd /app && python3 -c "
import torch
for split in ['train','val','public_test']:
    p = torch.load(f'data/{split}/trajectories.pt', map_location='cpu', weights_only=True)
    t = p['trajectories']
    print(f'{split}: shape={tuple(t.shape)}, dtype={t.dtype}, keys={list(p.keys())}')
    print(f'   mean={t.mean():.4f}, std={t.std():.4f}, min={t.min():.4f}, max={t.max():.4f}')
"

output

train: shape=(200, 100, 128), dtype=torch.float32, keys=['trajectories']
   mean=-0.0000, std=1.1600, min=-3.2449, max=3.2476
val: shape=(32, 100, 128), dtype=torch.float32, keys=['trajectories']
   mean=-0.0000, std=1.1339, min=-3.1972, max=3.2103
public_test: shape=(16, 100, 128), dtype=torch.float32, keys=['trajectories']
   mean=0.0000, std=1.2252, min=-3.1724, max=3.1835

Let me understand the data better. The trajectories have 100 timesteps each, 128 spatial points. The KS equation with L=22 is the classic chaotic regime (L=22 gives chaos). dt_effective=0.25. Let me think about the task: - Input: window of k=4 frames [B, 4, 128] - Output: next frame [B, 1, 128] - Evaluation: autoregressive rollout for 20 steps, scored on full rollout with rel L2. Key insight: The grader rolls out autoregressively for 20 steps. So the model needs to be stable under autoregressive rollout. Training only on single-step prediction (teacher forcing) leads to error accumulation. The key improvement here is to train with **multi-step rollout / pushforward** so that the model sees its own predictions and learns to be stable. Let me understand the evaluation more precisely. The grader: - Takes the last k=4 frames as input window - Predicts next frame - Rolls autoregressively for 20 steps - Scores full rollout with rollout_rel_l2_score Let me think about how the rollout works. Given a trajectory of 100 frames, presumably it takes frames [t-4:t] for some starting t, then predicts 20 steps forward, comparing to ground truth frames [t:t+20]. Let me verify the scoring metric. "rollout_rel_l2_score" - likely a score derived from relative L2 error, higher is better. Let me think: rel_l2 = ||pred - true|| / ||true||. A score might be something like max(0, 1 - rel_l2) or exp(-rel_l2) or 1/(1+rel_l2). Let me look more carefully at the data to understand the dynamics. Let me check the temporal correlation - how much does a frame change step-to-step.

Let me understand the temporal dynamics to gauge how hard single-step prediction is and how errors might compound.

bash

cd /app && python3 -c "
import torch
t = torch.load('data/train/trajectories.pt', map_location='cpu', weights_only=True)['trajectories']
# step-to-step change relative magnitude
def rel_l2(a,b): return ((a-b).pow(2).sum(-1).sqrt()/b.pow(2).sum(-1).sqrt()).mean().item()
# persistence baseline: predict next = current
print('persistence 1-step rel_l2:', rel_l2(t[:,:-1], t[:,1:]))
# persistence over 20 steps from frame 4
errs=[]
for h in range(1,21):
    errs.append(rel_l2(t[:,4:4+1].expand(-1,1,-1) if False else t[:,3:3+1], t[:,3+h:3+h+1]))
print('persistence rollout rel_l2 (predict frame3 for all):', sum(errs)/len(errs))
# typical norm
print('frame L2 norm mean:', t.pow(2).sum(-1).sqrt().mean().item())
# how fast does autocorrelation decay
f0=t[:,50]
for dt in [1,2,4,8,16,20]:
    print(f'  rel_l2 between frame 50 and 50+{dt}:', rel_l2(t[:,50:51], t[:,50+dt:50+dt+1]))
"

output

persistence 1-step rel_l2: 0.03789564222097397
persistence rollout rel_l2 (predict frame3 for all): 0.3303494594991207
frame L2 norm mean: 12.913924217224121
  rel_l2 between frame 50 and 50+1: 0.036414001137018204
  rel_l2 between frame 50 and 50+2: 0.07310735434293747
  rel_l2 between frame 50 and 50+4: 0.1470288336277008
  rel_l2 between frame 50 and 50+8: 0.29365140199661255
  rel_l2 between frame 50 and 50+16: 0.5589736700057983
  rel_l2 between frame 50 and 50+20: 0.6782833933830261

So the dynamics: - One step change is about 3.8% rel L2 - Over 20 steps persistence gives ~68% rel L2 (very different) So the model needs to predict step changes accurately and remain stable over 20 steps. This is the classic neural operator rollout problem. Key improvements I should make: 1. **Multi-step / pushforward training**: Train the model to roll out multiple steps and penalize the accumulated error. This is the most important thing for rollout stability. The "pushforward trick" (Brandstetter et al.) unrolls but only backprops through the last step. Or full backprop through rollout (BPTT). Given the small model and data, full BPTT through a few steps is feasible. 2. **Better loss**: Relative L2 loss (matching the eval metric) rather than plain MSE. Possibly a spectral loss term to match energy spectrum. Actually since eval is rel L2, use rel L2 loss or a combination. 3. **EMA** of weights for stability. 4. **More epochs** with cosine schedule and warmup. 5. **Data augmentation**: The KS equation on periodic domain has symmetries: - Spatial translation invariance (periodic shift) , can roll the array along x. - Reflection symmetry: x -> -x. Under KS, u(x,t) -> u(-x,t) is also a solution? Let's check. KS: u_t + u u_x + u_xx + u_xxxx = 0. Under x->-x: let v(x,t)=u(-x,t). Then v_x = -u_x(-x), v_xx=u_xx(-x), v_xxxx=u_xxxx(-x). v_t=u_t(-x). Plug: u_t(-x) + v v_x + v_xx + v_xxxx = u_t(-x) + u(-x)*(-u_x(-x)) + u_xx(-x) + u_xxxx(-x) = u_t(-x) - u u_x(-x) + u_xx(-x)+u_xxxx(-x). The original equation at -x says u_t(-x) + u u_x(-x) + u_xx(-x) + u_xxxx(-x)=0. So we have a sign difference on the u u_x term. So reflection alone is NOT a symmetry. But reflection combined with sign flip? Let v(x,t) = -u(-x,t). Then v_x = u_x(-x), v_xx = -u_xx(-x), v_xxxx = -u_xxxx(-x), v_t = -u_t(-x). v v_x = -u(-x)*u_x(-x). Plug: -u_t(-x) - u u_x(-x) - u_xx(-x) - u_xxxx(-x) = -(u_t + u u_x + u_xx + u_xxxx)(-x) = 0. Yes! So v(x,t) = -u(-x,t) is a solution. So the reflection+negation is a symmetry: u(x) -> -u(-x). So augmentations: - Spatial roll (translation) , periodic shift by any integer (or even sub-pixel via FFT, but integer is simplest and exact). This is a valid symmetry and multiplies data effectively. - Reflection with negation: u(x) -> -u(-x). These augmentations preserve the dynamics, so applying them to full windows+targets is valid. Translation is the big one: 128 possible shifts × 200 trajectories × 96 windows. Huge effective dataset. Actually wait , the FNO uses a grid coordinate channel appended (linspace 0..1). Translation invariance is slightly broken by the explicit grid channel... but actually the grid is just positional; since the conv and spectral ops, plus the appended grid. Hmm. The grid channel means the model could in principle use absolute position, but for a translation-invariant PDE the grid channel is actually not needed. But it's part of the fixed architecture. If we augment with random rolls, the model will learn to ignore the grid channel (since the same physical pattern appears at all positions), which is good for generalization. That's fine. Actually, augmenting with translation is essentially forcing translation invariance which is correct for this PDE. Good. Let me think about what matters most. The eval is autoregressive rollout rel L2 over 20 steps. The single most impactful technique is training on multi-step rollouts (unrolled training) with the loss computed at each step. Let me design the training. Let me think about the rollout mechanics carefully. The model input is [B,4,128] = last 4 frames. Output [B,1,128] = next frame. For autoregressive rollout: - window = [f0,f1,f2,f3] -> pred f4 - window = [f1,f2,f3,f4_pred] -> pred f5 - etc. So for multi-step training, I take a window of 4 consecutive ground-truth frames, predict forward R steps, each time sliding the window and appending the prediction, and compare each predicted frame to ground truth. For this I need training …[truncated]

The data shows ~3.8% per-step change but ~68% divergence over 20 steps, confirming this is a rollout-stability problem where errors compound. Key symmetries of KS on a periodic domain: spatial translation, and reflection-with-negation `u(x) → -u(-x)`. Let me verify model params and set up a baseline.

bash

cd /app && python3 -c "
import json, torch
from candidate_model import build_model
cfg=json.load(open('task_config.json'))
m=build_model(cfg)
n=sum(p.numel() for p in m.parameters())
print('total params:', n)
x=torch.randn(8,4,128)
y=m(x)
print('in',x.shape,'-> out',y.shape)
print('grid buffer persistent?:', '_grid' in dict(m.named_buffers()))
print('state_dict keys sample:', list(m.state_dict().keys())[:4])
"

output

total params: 71041
in torch.Size([8, 4, 128]) -> out torch.Size([8, 1, 128])
grid buffer persistent?: True
state_dict keys sample: ['lift.weight', 'lift.bias', 'blocks.0.spectral.weight', 'blocks.0.bypass.weight']

Wait, `_grid` buffer is registered with persistent=False but it shows up as a named_buffer. Let me check if it's in state_dict. persistent=False means it won't be in state_dict. Let me verify, because that matters for load compatibility.

bash

cd /app && python3 -c "
import json, torch
from candidate_model import build_model
cfg=json.load(open('task_config.json'))
m=build_model(cfg)
sd=m.state_dict()
print('_grid in state_dict:', any('grid' in k for k in sd.keys()))
print('num state_dict entries:', len(sd))
# test that a fresh model can load a saved state dict with weights_only
torch.save({'state_dict':sd,'feat_mean':0.0,'feat_std':1.0}, '/tmp/t.pt')
d=torch.load('/tmp/t.pt', weights_only=True)
m2=build_model(cfg); m2.load_state_dict(d['state_dict']); print('reload OK, feat_mean',d['feat_mean'])
"

output

_grid in state_dict: False
num state_dict entries: 18
reload OK, feat_mean 0.0

Good. The `_grid` buffer is non-persistent, so it's not in state_dict, which is fine. The output contract is satisfied by saving `state_dict`, `feat_mean`, `feat_std`. Now let me build a local evaluation harness that mimics autoregressive 20-step rollout, then run the baseline to get reference numbers. Let me write a shared eval module. Let me think about the rollout evaluation start index. I'll evaluate rollout starting from the first window (frames 0:4 predict 4:24), but also test a couple of other start indices to be robust. Actually, since the grader says "rolls your model autoregressively for 20 steps and scores the full rollout," and trajectories are length 100, they likely use frames [0:4] as seed and score [4:24], OR they might use a window near the start. To be safe and representative, I'll evaluate from multiple start points and average. But the most likely single choice is seed = first 4 frames. Let me write eval to compute rollout rel L2 from start index s=4 (seed 0:4) over horizon 20. I'll also compute averaged over several starts for a robustness view. Let me create an eval helper and measure the baseline model. First, let me train the baseline quickly (2 epochs) and also 10 epochs to see where it lands. Actually, let me just write my own comprehensive training script and an eval harness. But first, get baseline numbers by running provided train.py with 10 epochs (its default) , but that may take a while. Let me time 2 epochs first.

The contract is satisfied by saving `state_dict`, `feat_mean`, `feat_std` (the `_grid` buffer is non-persistent). Let me create a local evaluation harness that mimics the grader's 20-step autoregressive rollout, then measure the baseline.

str_replace_based_edit_tool

command: create
path: /app/eval_rollout.py
file_text: from __future__ import annotations
import json, os, sys
from pathlib import Path
import torch
sys.path.insert(0, str(Path(__file__).resolve().parent))
from candidate_model import build_model

APP = Path("/app")


@torch.no_grad()
def rollout_metrics(model, traj, feat_mean, feat_std, k=4, horizon=20, device="cpu",
                    starts=None):
    """Autoregressive rollout mimicking the grader.
    traj: [N, T, X]. For each start s, seed = frames[s-k:s], predict horizon frames,
    compare to frames[s:s+horizon]. Returns dict of metrics averaged over starts+traj.
    """
    model.eval()
    N, T, X = traj.shape
    if starts is None:
        starts = [k]  # seed = first k frames (0:k), predict k:k+horizon
    per_step_rel = torch.zeros(horizon)
    agg_num = 0.0
    agg_den = 0.0
    rmse_acc = 0.0
    count = 0
    final_rel = 0.0
    for s in starts:
        if s - k < 0 or s + horizon > T:
            continue
        window = traj[:, s - k:s].clone().to(device)  # [N,k,X]
        window = (window - feat_mean) / feat_std
        preds = []
        for h in range(horizon):
            out = model(window)  # [N,1,X] normalized
            preds.append(out)
            window = torch.cat([window[:, 1:], out], dim=1)
        pred = torch.cat(preds, dim=1) * feat_std + feat_mean  # [N,horizon,X]
        true = traj[:, s:s + horizon].to(device)
        # per-step rel l2
        num = (pred - true).pow(2).sum(-1).sqrt()        # [N,horizon]
        den = true.pow(2).sum(-1).sqrt().clamp_min(1e-8) # [N,horizon]
        rel = (num / den)                                # [N,horizon]
        per_step_rel += rel.mean(0).cpu()
        final_rel += rel[:, -1].mean().item()
        # aggregate rel l2 over whole block
        agg_num += (pred - true).pow(2).sum().item()
        agg_den += true.pow(2).sum().item()
        rmse_acc += (pred - true).pow(2).mean().item()
        count += 1
    per_step_rel /= count
    res = {
        "rollout_rel_l2_mean": per_step_rel.mean().item(), # mean over steps of per-step rel l2
        "rollout_rel_l2_agg": (agg_num ** 0.5) / (agg_den ** 0.5),
        "final_step_rel_l2": final_rel / count,
        "rollout_rmse": (rmse_acc / count) ** 0.5,
        "per_step": per_step_rel.tolist(),
    }
    return res


def load_model(ckpt_path, cfg, device="cpu"):
    d = torch.load(ckpt_path, weights_only=True, map_location=device)
    m = build_model(cfg).to(device)
    m.load_state_dict(d["state_dict"])
    return m, float(d["feat_mean"]), float(d["feat_std"])


if __name__ == "__main__":
    cfg = json.load(open(APP / "task_config.json"))
    device = "cuda" if torch.cuda.is_available() else "cpu"
    ckpt = sys.argv[1] if len(sys.argv) > 1 else str(APP / "outputs" / "model.pt")
    m, fm, fs = load_model(ckpt, cfg, device)
    for split in ["val", "public_test"]:
        traj = torch.load(APP / "data" / split / "trajectories.pt", weights_only=True)["trajectories"].float()
        # evaluate from first window, and from several starts for robustness
        r1 = rollout_metrics(m, traj, fm, fs, device=device, starts=[4])
        rmulti = rollout_metrics(m, traj, fm, fs, device=device, starts=[4, 20, 40, 60])
        print(f"[{split}] seed@4  rel_l2_mean={r1['rollout_rel_l2_mean']:.4f} "
              f"agg={r1['rollout_rel_l2_agg']:.4f} final={r1['final_step_rel_l2']:.4f} rmse={r1['rollout_rmse']:.4f}")
        print(f"[{split}] multi   rel_l2_mean={rmulti['rollout_rel_l2_mean']:.4f} "
              f"agg={rmulti['rollout_rel_l2_agg']:.4f} final={rmulti['final_step_rel_l2']:.4f}")
        print(f"[{split}] per_step[0,1,2,4,9,19]=", [round(r1['per_step'][i],3) for i in [0,1,2,4,9,19]])

output

New file created successfully at /app/eval_rollout.py

Now let me run the baseline training (default 10 epochs) to establish reference numbers, timing it.

bash

cd /app && time python3 train.py 2>&1 | tail -20

output

[train] device=cuda epochs=10 pairs=19200
[train] epoch=1/10 mse=6.0238e-02 val_rel_l2=0.0136
[train] epoch=2/10 mse=1.3373e-04 val_rel_l2=0.0076
[train] epoch=3/10 mse=6.1280e-05 val_rel_l2=0.0058
[train] epoch=4/10 mse=3.6445e-05 val_rel_l2=0.0046
[train] epoch=5/10 mse=2.4894e-05 val_rel_l2=0.0038
[train] epoch=6/10 mse=1.8479e-05 val_rel_l2=0.0034
[train] epoch=7/10 mse=1.4761e-05 val_rel_l2=0.0032
[train] epoch=8/10 mse=1.2601e-05 val_rel_l2=0.0030
[train] epoch=9/10 mse=1.1432e-05 val_rel_l2=0.0029
[train] epoch=10/10 mse=1.0889e-05 val_rel_l2=0.0028
[train] wrote /app/outputs/model.pt
[stderr]
real	0m40.400s
user	0m49.940s
sys	0m3.040s

The baseline gets single-step val rel L2 of 0.0028, which is very good for single-step. But the real question is the 20-step rollout. Let me evaluate the baseline with my rollout harness.

The baseline reaches great single-step val rel L2 (0.0028) in 40s. But the grader scores 20-step rollout , let me measure that, which is what actually matters.

bash

cd /app && python3 eval_rollout.py 2>&1 | tail -12

output

[val] seed@4  rel_l2_mean=0.0099 agg=0.0146 final=0.0192 rmse=0.0171
[val] multi   rel_l2_mean=0.0094 agg=0.0138 final=0.0180
[val] per_step[0,1,2,4,9,19]= [0.003, 0.003, 0.004, 0.006, 0.009, 0.019]
[public_test] seed@4  rel_l2_mean=0.0082 agg=0.0148 final=0.0149 rmse=0.0186
[public_test] multi   rel_l2_mean=0.0078 agg=0.0121 final=0.0146
[public_test] per_step[0,1,2,4,9,19]= [0.002, 0.003, 0.003, 0.005, 0.008, 0.015]

Interesting! The baseline is actually quite strong already. Over 20 steps, the rel L2 grows from 0.003 (step 1) to 0.019 (step 20) on val. Mean rollout rel L2 ~0.0099, final step ~0.019. So the baseline is already good. But there is room for improvement in rollout stability. The error compounds from 0.003 → 0.019 (about 6x over 20 steps). My goal: reduce the accumulated rollout error. The key techniques to improve rollout: 1. Multi-step / BPTT rollout training so the model directly minimizes compounding error. 2. Data augmentation (translation + reflection-negation) for better generalization. 3. EMA for smoother weights. 4. Relative L2 loss matching the metric. 5. Longer training. Let me now build an improved training script. Let me think about the design carefully. **Data layout**: I'll precompute sequences. For multi-step training with rollout length R, I need windows of k + R consecutive frames. From each trajectory of length 100, I can extract many. I'll build an index of (traj_idx, start_t) where the sequence frames [start_t : start_t + k + R] are valid. Then for a batch I gather [B, k+R, X]. But R changes during curriculum. Simplest: for a given max R, index requires k+maxR frames. But for smaller R in early epochs, I can use more start positions. To keep it simple and fast, I'll just require k + R_max frames and reuse the same index; the number of sequences is 200*(100 - (k+R_max) + 1). For R_max=10, k=4: 200*(100-14+1)=200*87=17400 sequences. Plenty. Actually, I'd like to keep single-step data maximally used too. But multi-step sequences already contain single-step info (first step). Let me just do the curriculum with sequences of length k+R_max and slice. Hmm, but if I fix the index to require k+R_max frames, then in early epochs with R=1 I only use those start positions (still 17400, fine). Good enough. **Augmentation** (applied per-batch on GPU, to the whole [B, k+R, X] sequence consistently): - Random spatial roll: shift along X by random integer per-sample. Use torch.roll per sample , but roll with different shift per sample requires gather. I can do it via building an index. Or do a single random shift per batch (cheaper, still effective across epochs). Per-sample is better. Let me implement per-sample roll via gather with a per-sample shift. - Reflection-negation: with prob 0.5, u -> -flip(u) along X. Applied to whole sequence. Both are exact symmetries so targets transform the same way (they're part of the same sequence), so I just transform the entire [B, k+R, X] block before splitting into window/targets. Wait, need to be careful: reflection-negation is u(x) -> -u(-x). On a periodic grid of size X with points x_j = j/X * L, "-x" maps index j -> (-j) mod X = (X - j) mod X. That's flip plus a roll by 1? Let me think: u(-x_j) where -x_j corresponds to index (X-j) mod X. For j=0 -> 0, j=1 -> X-1, etc. torch.flip reverses to index (X-1-j). So flip gives u(x_{X-1-j}) = u at index X-1-j. We want index (X-j) mod X = X-j for j>=1, and 0 for j=0. So flip(u)[j] = u[X-1-j], but we want u[(X-j) mod X]. These differ by a roll of 1: (X-j) mod X = (X-1-j) + 1 mod X. So u[(X-j)%X] = flip(u) rolled by +1... Let me just verify: define v[j] = u[(X - j) % X]. For j=0: u[0]. j=1: u[X-1]. j=2: u[X-2]. flip(u)[j]=u[X-1-j]: j=0->u[X-1], j=1->u[X-2]. So v = roll(flip(u), shift=1)? roll(flip(u),1)[j] = flip(u)[(j-1)%X] = u[X-1-((j-1)%X)]. j=0: (j-1)%X = X-1 -> u[X-1-(X-1)]=u[0]. j=1: (0)%X=0 -> u[X-1]. j=2: 1 -> u[X-2]. Yes! v = roll(flip(u, dim=X), shift=1, dim=X). But honestly, since I'm also doing random rolls as augmentation, the roll-by-1 difference is absorbed into the random roll distribution. So I can just use -flip(u) and it's still a valid sample from the symmetry-augmented distribution (the extra roll by 1 is covered by random translation). Actually to be rigorous and since translation is already a full symmetry I apply randomly, combining reflection (flip) + negation + random roll covers the exact reflection-ne …[truncated]

The baseline is already decent: rollout rel L2 grows from 0.003 (step 1) to 0.019 (step 20), mean ~0.0099 on val. The error compounds ~6× over the rollout. My plan to reduce compounding error: 1. **Multi-step rollout (BPTT) training with a curriculum** , directly optimize the autoregressive rollout, which is what's graded. 2. **Relative-L2 loss** matching the eval metric. 3. **Symmetry augmentation** , random periodic translation + reflection-with-negation `u→-flip(u)` (both exact KS symmetries). 4. **EMA** weights + warmup/cosine schedule, more epochs. Let me write the improved trainer with these as configurable options.

str_replace_based_edit_tool

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

APP = Path(os.environ.get("APP_DIR", "/app"))
sys.path.insert(0, str(APP))
from candidate_model import build_model
from dataset import KSForecast, compute_standardization
from eval_rollout import rollout_metrics


def getenv_f(name, default):
    v = os.environ.get(name)
    return float(v) if v is not None else default


def getenv_i(name, default):
    v = os.environ.get(name)
    return int(v) if v is not None else default


def parse_curriculum(s):
    # format: "e:R,e:R,..." meaning from epoch e (0-based) use rollout length R
    out = []
    for part in s.split(","):
        e, r = part.split(":")
        out.append((int(e), int(r)))
    return sorted(out)


def rollout_len_for_epoch(curr, epoch):
    r = curr[0][1]
    for e, rr in curr:
        if epoch >= e:
            r = rr
    return r


def augment(seq, do_roll=True, do_refl=True):
    # seq: [B, L, X] -> apply per-sample translation + reflection-negation
    B, L, X = seq.shape
    if do_roll:
        shifts = torch.randint(0, X, (B,), device=seq.device)
        ar = torch.arange(X, device=seq.device).view(1, X)
        idx = (ar - shifts.view(B, 1)) % X            # gather index per sample
        idx = idx.view(B, 1, X).expand(B, L, X)
        seq = torch.gather(seq, 2, idx)
    if do_refl:
        mask = torch.rand(B, device=seq.device) < 0.5
        if mask.any():
            refl = -torch.flip(seq, dims=[2])
            seq = torch.where(mask.view(B, 1, 1), refl, seq)
    return seq


class EMA:
    def __init__(self, model, decay):
        self.decay = decay
        self.shadow = {k: v.detach().clone() for k, v in model.state_dict().items()}

    def update(self, model):
        d = self.decay
        for k, v in model.state_dict().items():
            s = self.shadow[k]
            if v.dtype.is_floating_point:
                s.mul_(d).add_(v.detach(), alpha=1 - d)
            else:
                s.copy_(v)

    def copy_to(self, model):
        model.load_state_dict(self.shadow, strict=True)


def main():
    cfg = json.load(open(APP / "task_config.json"))
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    seed = getenv_i("SEED", int(cfg["seed"]))
    torch.manual_seed(seed)
    k = int(cfg["in_channels"])

    # hyperparams
    epochs = getenv_i("TASK_EPOCHS_OVERRIDE", getenv_i("EPOCHS", 50))
    bs = getenv_i("BS", 128)
    lr = getenv_f("LR", 2e-3)
    wd = getenv_f("WD", 1e-4)
    warmup = getenv_i("WARMUP", 3)
    ema_decay = getenv_f("EMA", 0.999)
    grad_clip = getenv_f("GRAD_CLIP", 1.0)
    noise = getenv_f("NOISE", 0.0)          # input-noise std (in normalized units)
    do_roll = getenv_i("AUG_ROLL", 1) == 1
    do_refl = getenv_i("AUG_REFL", 1) == 1
    curriculum = parse_curriculum(os.environ.get("CURR", "0:1,3:2,6:4,12:6,20:8,30:10"))
    Rmax = max(r for _, r in curriculum)
    loss_kind = os.environ.get("LOSS", "rel")   # 'rel' or 'mse' or 'mix'
    save_path = Path(os.environ.get("SAVE", APP / "outputs" / "model.pt"))

    train_ds = KSForecast(APP / "data" / "train", k=k)
    feat_mean, feat_std = compute_standardization(train_ds.trajectories)
    traj = train_ds.trajectories.to(device)            # [N,T,X]
    N, T, X = traj.shape
    val_traj = torch.load(APP / "data" / "val" / "trajectories.pt", weights_only=True)["trajectories"].float()

    # sequence index: need k+Rmax consecutive frames
    Lseq = k + Rmax
    starts = []
    for i in range(N):
        for t0 in range(0, T - Lseq + 1):
            starts.append((i, t0))
    starts = torch.tensor(starts, dtype=torch.long, device=device)
    print(f"[cfg] epochs={epochs} bs={bs} lr={lr} wd={wd} warmup={warmup} ema={ema_decay} "
          f"Rmax={Rmax} loss={loss_kind} noise={noise} roll={do_roll} refl={do_refl} "
          f"seqs={len(starts)} curr={curr
…[truncated]

output

New file created successfully at /app/train_improved.py

Let me first do a quick sanity test with a short run (few epochs, small curriculum) to make sure everything works and to time it.

Let me sanity-check the script with a short run to confirm it works and to measure timing.

bash

cd /app && EPOCHS=4 CURR="0:1,2:4" SAVE=/tmp/test_model.pt python3 train_improved.py 2>&1 | tail -12

output

[cfg] epochs=4 bs=128 lr=0.002 wd=0.0001 warmup=3 ema=0.999 Rmax=4 loss=rel noise=0.0 roll=True refl=True seqs=18600 curr=[(0, 1), (2, 4)]
[ep 1/4] R=1 lr=6.71e-04 loss=5.3147e-01 val_roll_mean=0.9933 final=1.0119 agg=0.9924 *  (6s)
[ep 2/4] R=1 lr=1.34e-03 loss=3.2372e-02 val_roll_mean=0.9831 final=1.0100 agg=0.9831 *  (7s)
[ep 3/4] R=4 lr=2.00e-03 loss=4.3885e-02 val_roll_mean=0.9670 final=1.0083 agg=0.9684 *  (11s)
[ep 4/4] R=4 lr=0.00e+00 loss=2.6277e-02 val_roll_mean=0.9483 final=1.0068 agg=0.9516 *  (14s)
[done] best val_roll_mean=0.9483 -> wrote /tmp/test_model.pt

Something is wrong. The val_roll_mean is ~0.95 (terrible) even though training loss is dropping. The issue is likely the EMA decay , with decay 0.999 and only ~145 steps per epoch × 4 = 580 steps, the EMA is still dominated by the initial random weights! EMA decay 0.999 has a time constant of ~1000 steps, so after 580 steps the shadow is still mostly the random init. This is the classic EMA warmup problem. For short runs, EMA needs either lower decay or more steps. For a real run of 50 epochs (~145*50 = 7250 steps), EMA 0.999 is fine (time constant 1000 steps << 7250). But evaluation of EMA early will look bad. But wait, even at epoch 4 with 580 steps, EMA 0.999 means weight on init ≈ 0.999^580 ≈ 0.56. So 56% of the random init remains. That explains the terrible metric. For the short test, let me not worry. But I should use EMA decay that adapts, or use a "warmup" for EMA, or just rely on longer training. Let me also evaluate the RAW (non-EMA) model to compare, so I can pick whichever is better. Let me add evaluation of both raw and EMA, and save the best across both. Actually, a cleaner approach: use EMA with a dynamic decay that ramps up, e.g. decay_t = min(ema_decay, (1+step)/(10+step)). This is the common "EMA warmup" used in many libraries. This makes EMA track the fast weights early and converge to ema_decay later. Let me implement that. Also I should track and possibly save the best of raw vs EMA. Let me evaluate both each epoch. Let me also reconsider: maybe the raw model is already much better. Let me modify to eval both, and use dynamic EMA decay. Let me update the script.

The EMA with fixed decay 0.999 is dominated by the random initialization for short runs (0.999^580 ≈ 0.56 weight still on init). I'll add EMA decay warmup and evaluate **both** raw and EMA weights each epoch, saving whichever is best.

str_replace_based_edit_tool

command: str_replace
path: /app/train_improved.py
old_str: class EMA:
    def __init__(self, model, decay):
        self.decay = decay
        self.shadow = {k: v.detach().clone() for k, v in model.state_dict().items()}

    def update(self, model):
        d = self.decay
        for k, v in model.state_dict().items():
new_str: class EMA:
    def __init__(self, model, decay):
        self.decay = decay
        self.num_updates = 0
        self.shadow = {k: v.detach().clone() for k, v in model.state_dict().items()}

    def update(self, model):
        self.num_updates += 1
        # decay warmup so EMA tracks fast weights early, converges to self.decay later
        d = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates))
        for k, v in model.state_dict().items():

output

The file /app/train_improved.py has been edited successfully.

str_replace_based_edit_tool

command: str_replace
path: /app/train_improved.py
old_str:         # eval EMA weights via rollout
        eval_model = build_model(cfg).to(device)
        ema.copy_to(eval_model)
        r = rollout_metrics(eval_model, val_traj, feat_mean, feat_std, device=device, starts=[4])
        metric = r["rollout_rel_l2_mean"]
        flag = ""
        if metric < best_metric:
            best_metric = metric
            best = {kk: vv.detach().cpu().clone() for kk, vv in ema.shadow.items()}
            flag = " *"
        print(f"[ep {epoch+1}/{epochs}] R={R} lr={lr_at(gstep):.2e} loss={running/max(nb,1):.4e} "
              f"val_roll_mean={metric:.4f} final={r['final_step_rel_l2']:.4f} "
              f"agg={r['rollout_rel_l2_agg']:.4f}{flag}  ({time.time()-t_start:.0f}s)", flush=True)
new_str:         # eval BOTH raw and EMA weights via rollout; keep the better
        r_raw = rollout_metrics(model, val_traj, feat_mean, feat_std, device=device, starts=[4])
        eval_model = build_model(cfg).to(device)
        ema.copy_to(eval_model)
        r_ema = rollout_metrics(eval_model, val_traj, feat_mean, feat_std, device=device, starts=[4])
        m_raw, m_ema = r_raw["rollout_rel_l2_mean"], r_ema["rollout_rel_l2_mean"]
        if m_ema <= m_raw:
            metric, r, which, sd = m_ema, r_ema, "ema", ema.shadow
        else:
            metric, r, which, sd = m_raw, r_raw, "raw", model.state_dict()
        flag = ""
        if metric < best_metric:
            best_metric = metric
            best = {kk: vv.detach().cpu().clone() for kk, vv in sd.items()}
            flag = " *"
        print(f"[ep {epoch+1}/{epochs}] R={R} lr={lr_at(gstep):.2e} loss={running/max(nb,1):.4e} "
              f"val[{which}]={metric:.4f} (raw={m_raw:.4f} ema={m_ema:.4f}) final={r['final_step_rel_l2']:.4f} "
              f"agg={r['rollout_rel_l2_agg']:.4f}{flag}  ({time.time()-t_start:.0f}s)", flush=True)

output

The file /app/train_improved.py has been edited successfully.

bash

cd /app && EPOCHS=6 CURR="0:1,2:2,4:4" SAVE=/tmp/test_model.pt python3 train_improved.py 2>&1 | tail -12

output

[cfg] epochs=6 bs=128 lr=0.002 wd=0.0001 warmup=3 ema=0.999 Rmax=4 loss=rel noise=0.0 roll=True refl=True seqs=18600 curr=[(0, 1), (2, 2), (4, 4)]
[ep 1/6] R=1 lr=6.71e-04 loss=5.3147e-01 val[raw]=0.0963 (raw=0.0963 ema=0.1310) final=0.1768 agg=0.1292 *  (3s)
[ep 2/6] R=1 lr=1.34e-03 loss=3.2372e-02 val[ema]=0.0706 (raw=0.0966 ema=0.0706) final=0.1353 agg=0.0855 *  (5s)
[ep 3/6] R=2 lr=2.00e-03 loss=2.7192e-02 val[ema]=0.0396 (raw=0.0765 ema=0.0396) final=0.0654 agg=0.0469 *  (7s)
[ep 4/6] R=2 lr=1.50e-03 loss=2.3060e-02 val[ema]=0.0536 (raw=0.0662 ema=0.0536) final=0.0861 agg=0.0692  (9s)
[ep 5/6] R=4 lr=5.00e-04 loss=1.5734e-02 val[ema]=0.0206 (raw=0.0284 ema=0.0206) final=0.0348 agg=0.0286 *  (13s)
[ep 6/6] R=4 lr=0.00e+00 loss=6.5094e-03 val[raw]=0.0153 (raw=0.0153 ema=0.0156) final=0.0255 agg=0.0246 *  (16s)
[done] best val_roll_mean=0.0153 -> wrote /tmp/test_model.pt

Now it's working. With the EMA warmup, results look reasonable. After just 6 short epochs, we get to 0.0153 (vs baseline 0.0099 at 10 epochs). The curriculum and more epochs should help. Note EMA is better early, raw better at the very end as LR→0 (EMA lags). This confirms evaluating both is the right call. Now let me do a proper longer run. Let me think about the curriculum and epochs. Each epoch at R=4 is ~3-4s, R=8 ~6-8s, R=10 ~8-10s. So a 50-epoch run reaching R=10 might be ~6-8 min. Very affordable. Let me push for a solid configuration. Let me think about the best curriculum. The eval horizon is 20. Training at larger R should help the model be stable over long rollouts, but full BPTT at large R can be harder to optimize and slower. Let me try R up to 10 or 12. Let me also consider whether to hold some epochs at the max R with decaying LR to refine. Let me design: - warmup=3 epochs - curriculum: 0:1, 2:2, 5:4, 10:6, 16:8, 24:10, (maybe 34:12) - epochs=50 Let me run this and watch. But before committing to a long run, let me reconsider the loss and a couple of design points by doing a medium run (~30 epochs) and compare against baseline on BOTH val and public_test using eval_rollout.py. Actually, let me just run a full 50-epoch config now since it's cheap, and evaluate. I'll keep the test output model separate so I don't overwrite /app/outputs until I confirm improvement. Let me reconsider one thing: the metric the grader uses likely evaluates from a specific start. My training uses all windows including near the start, so it's robust. And I evaluate val from start=4. Good. Let me also reconsider: should I train at R that matches eval horizon 20 near the end? Let me try extending to R=12 and see. But BPTT at R=20 is memory/time heavier and can cause vanishing/exploding grads. Grad clipping helps. Let me cap at 12 for main run, then maybe fine-tune. Let me run the main config. Let me launch a 50-epoch run in the background and monitor. Actually, let me run it in foreground with tail since it's only ~6-8 min. But that might approach time limits for a single command (300s = 5 min). Let me run in background and poll.

Now it works correctly , EMA helps early, raw wins as LR→0, so evaluating both is worthwhile. After only 6 short epochs we're at 0.0153. Let me launch a proper longer run with a fuller curriculum in the background and monitor it.

bash

cd /app && rm -f /tmp/run1.log && nohup env EPOCHS=50 WARMUP=3 LR=2e-3 \
  CURR="0:1,2:2,5:4,10:6,16:8,24:10,36:12" \
  SAVE=/tmp/model_run1.pt python3 train_improved.py > /tmp/run1.log 2>&1 &
echo "launched pid $!"

output

launched pid 221

bash

sleep 60 && sed -n '1,40p' /tmp/run1.log

output

[cfg] epochs=50 bs=128 lr=0.002 wd=0.0001 warmup=3 ema=0.999 Rmax=12 loss=rel noise=0.0 roll=True refl=True seqs=17000 curr=[(0, 1), (2, 2), (5, 4), (10, 6), (16, 8), (24, 10), (36, 12)]
[ep 1/50] R=1 lr=6.72e-04 loss=5.6149e-01 val[raw]=0.1379 (raw=0.1379 ema=0.1633) final=0.2365 agg=0.1770 *  (3s)
[ep 2/50] R=1 lr=1.34e-03 loss=3.0377e-02 val[ema]=0.1663 (raw=0.2327 ema=0.1663) final=0.3471 agg=0.1885  (4s)
[ep 3/50] R=2 lr=2.00e-03 loss=3.6886e-02 val[ema]=0.1070 (raw=0.1454 ema=0.1070) final=0.2181 agg=0.1199 *  (6s)
[ep 4/50] R=2 lr=2.00e-03 loss=3.9860e-02 val[ema]=0.0915 (raw=0.1236 ema=0.0915) final=0.1413 agg=0.1074 *  (8s)
[ep 5/50] R=2 lr=1.99e-03 loss=2.9339e-02 val[ema]=0.0349 (raw=0.0574 ema=0.0349) final=0.0587 agg=0.0422 *  (10s)
[ep 6/50] R=4 lr=1.98e-03 loss=2.3609e-02 val[ema]=0.0548 (raw=0.0634 ema=0.0548) final=0.1117 agg=0.0606  (13s)
[ep 7/50] R=4 lr=1.96e-03 loss=2.2878e-02 val[ema]=0.0803 (raw=0.0954 ema=0.0803) final=0.1647 agg=0.0932  (16s)
[ep 8/50] R=4 lr=1.94e-03 loss=3.9387e-02 val[raw]=0.0953 (raw=0.0953 ema=0.1129) final=0.1945 agg=0.1105  (19s)
[ep 9/50] R=4 lr=1.92e-03 loss=2.9496e-02 val[ema]=0.0493 (raw=0.0963 ema=0.0493) final=0.0831 agg=0.0657  (23s)
[ep 10/50] R=4 lr=1.89e-03 loss=2.8885e-02 val[ema]=0.0448 (raw=0.0518 ema=0.0448) final=0.0847 agg=0.0531  (27s)
[ep 11/50] R=6 lr=1.86e-03 loss=2.5497e-02 val[ema]=0.0311 (raw=0.0340 ema=0.0311) final=0.0518 agg=0.0390 *  (31s)
[ep 12/50] R=6 lr=1.82e-03 loss=1.7102e-02 val[ema]=0.0285 (raw=0.0345 ema=0.0285) final=0.0477 agg=0.0352 *  (36s)
[ep 13/50] R=6 lr=1.78e-03 loss=1.6341e-02 val[ema]=0.0230 (raw=0.0257 ema=0.0230) final=0.0414 agg=0.0272 *  (40s)
[ep 14/50] R=6 lr=1.74e-03 loss=1.5294e-02 val[ema]=0.0263 (raw=0.0325 ema=0.0263) final=0.0442 agg=0.0291  (45s)
[ep 15/50] R=6 lr=1.70e-03 loss=1.5014e-02 val[ema]=0.0301 (raw=0.0307 ema=0.0301) final=0.0560 agg=0.0337  (49s)
[ep 16/50] R=6 lr=1.65e-03 loss=2.1492e-02 val[raw]=0.0593 (raw=0.0593 ema=0.0701) final=0.1103 agg=0.0657  (53s)

The training is progressing but it's noisy, and at high LR (2e-3) with larger R the rollout loss bounces around (e.g., epoch 16 jumped to 0.059). The LR might be too high for stable BPTT at larger R. The best so far is 0.0230 at epoch 13, which is worse than baseline's 0.0099. But LR is still high (1.65e-3); the cosine will bring it down and refine later. Let me keep watching , the real improvement comes as LR decays. But I'm a bit concerned the high LR with large-R BPTT is causing instability. Let me let it continue and see where it ends up. Let me wait more.

Training is progressing but noisy at the high LR (2e-3) with large-R BPTT , best so far 0.023 at epoch 13, still above baseline. The refinement happens as cosine LR decays. Let me continue monitoring.

bash

sleep 90 && sed -n '16,45p' /tmp/run1.log

output

[ep 15/50] R=6 lr=1.70e-03 loss=1.5014e-02 val[ema]=0.0301 (raw=0.0307 ema=0.0301) final=0.0560 agg=0.0337  (49s)
[ep 16/50] R=6 lr=1.65e-03 loss=2.1492e-02 val[raw]=0.0593 (raw=0.0593 ema=0.0701) final=0.1103 agg=0.0657  (53s)
[ep 17/50] R=8 lr=1.59e-03 loss=3.1423e-02 val[raw]=0.0365 (raw=0.0365 ema=0.0846) final=0.0695 agg=0.0391  (59s)
[ep 18/50] R=8 lr=1.54e-03 loss=2.4804e-02 val[raw]=0.0256 (raw=0.0256 ema=0.0629) final=0.0381 agg=0.0279  (65s)
[ep 19/50] R=8 lr=1.48e-03 loss=1.3117e-02 val[raw]=0.0212 (raw=0.0212 ema=0.0402) final=0.0329 agg=0.0239 *  (70s)
[ep 20/50] R=8 lr=1.42e-03 loss=1.2224e-02 val[raw]=0.0215 (raw=0.0215 ema=0.0347) final=0.0317 agg=0.0240  (76s)
[ep 21/50] R=8 lr=1.36e-03 loss=1.1580e-02 val[raw]=0.0180 (raw=0.0180 ema=0.0268) final=0.0268 agg=0.0198 *  (82s)
[ep 22/50] R=8 lr=1.30e-03 loss=1.0868e-02 val[raw]=0.0167 (raw=0.0167 ema=0.0203) final=0.0243 agg=0.0211 *  (87s)
[ep 23/50] R=8 lr=1.23e-03 loss=1.0398e-02 val[raw]=0.0168 (raw=0.0168 ema=0.0189) final=0.0281 agg=0.0195  (93s)
[ep 24/50] R=8 lr=1.17e-03 loss=1.0198e-02 val[raw]=0.0168 (raw=0.0168 ema=0.0185) final=0.0238 agg=0.0183  (99s)
[ep 25/50] R=10 lr=1.10e-03 loss=1.0501e-02 val[raw]=0.0131 (raw=0.0131 ema=0.0180) final=0.0197 agg=0.0148 *  (106s)
[ep 26/50] R=10 lr=1.03e-03 loss=9.8420e-03 val[raw]=0.0146 (raw=0.0146 ema=0.0176) final=0.0214 agg=0.0168  (113s)
[ep 27/50] R=10 lr=9.67e-04 loss=9.3518e-03 val[raw]=0.0132 (raw=0.0132 ema=0.0178) final=0.0201 agg=0.0148  (119s)
[ep 28/50] R=10 lr=9.00e-04 loss=8.6792e-03 val[raw]=0.0128 (raw=0.0128 ema=0.0150) final=0.0189 agg=0.0138 *  (126s)
[ep 29/50] R=10 lr=8.34e-04 loss=1.8687e-02 val[raw]=0.0112 (raw=0.0112 ema=0.0202) final=0.0182 agg=0.0128 *  (133s)
[ep 30/50] R=10 lr=7.68e-04 loss=7.6634e-03 val[raw]=0.0105 (raw=0.0105 ema=0.0177) final=0.0154 agg=0.0111 *  (141s)
[ep 31/50] R=10 lr=7.04e-04 loss=6.9812e-03 val[raw]=0.0083 (raw=0.0083 ema=0.0153) final=0.0124 agg=0.0096 *  (148s)

Now as LR decays, the raw model is improving: epoch 31 hits 0.0083, better than baseline's 0.0099! And it's still improving with ~19 epochs to go (LR still 7e-4 decaying to 0). The EMA is now lagging behind the raw (because raw is improving fast as LR decays, and EMA averages older worse weights). Interesting. Let me let it finish. Note: at this stage raw is consistently better than EMA. That's because during the rapid-improvement phase EMA lags. EMA tends to help when training is noisy around a plateau. With a decaying LR converging, raw is fine. Keeping both and picking best is the right strategy. Let me wait for completion.

As LR decays, the raw model now beats the baseline , epoch 31 hits 0.0083 (vs 0.0099 baseline) and still improving. Let me wait for it to finish.

bash

sleep 120 && sed -n '31,55p' /tmp/run1.log

output

[ep 30/50] R=10 lr=7.68e-04 loss=7.6634e-03 val[raw]=0.0105 (raw=0.0105 ema=0.0177) final=0.0154 agg=0.0111 *  (141s)
[ep 31/50] R=10 lr=7.04e-04 loss=6.9812e-03 val[raw]=0.0083 (raw=0.0083 ema=0.0153) final=0.0124 agg=0.0096 *  (148s)
[ep 32/50] R=10 lr=6.41e-04 loss=6.5351e-03 val[raw]=0.0110 (raw=0.0110 ema=0.0147) final=0.0165 agg=0.0127  (155s)
[ep 33/50] R=10 lr=5.79e-04 loss=5.9657e-03 val[raw]=0.0094 (raw=0.0094 ema=0.0122) final=0.0152 agg=0.0114  (162s)
[ep 34/50] R=10 lr=5.19e-04 loss=5.5295e-03 val[raw]=0.0100 (raw=0.0100 ema=0.0127) final=0.0148 agg=0.0109  (169s)
[ep 35/50] R=10 lr=4.62e-04 loss=5.3712e-03 val[ema]=0.0217 (raw=0.0249 ema=0.0217) final=0.0364 agg=0.0253  (177s)
[ep 36/50] R=10 lr=4.07e-04 loss=1.0128e-02 val[raw]=0.0158 (raw=0.0158 ema=0.0170) final=0.0258 agg=0.0195  (184s)
[ep 37/50] R=12 lr=3.54e-04 loss=8.8016e-03 val[raw]=0.0083 (raw=0.0083 ema=0.0105) final=0.0124 agg=0.0092 *  (194s)
[ep 38/50] R=12 lr=3.05e-04 loss=4.9090e-03 val[raw]=0.0087 (raw=0.0087 ema=0.0103) final=0.0131 agg=0.0097  (203s)
[ep 39/50] R=12 lr=2.58e-04 loss=3.8933e-03 val[raw]=0.0047 (raw=0.0047 ema=0.0082) final=0.0067 agg=0.0053 *  (212s)
[ep 40/50] R=12 lr=2.15e-04 loss=3.1548e-03 val[raw]=0.0042 (raw=0.0042 ema=0.0073) final=0.0061 agg=0.0053 *  (220s)
[ep 41/50] R=12 lr=1.76e-04 loss=2.8508e-03 val[raw]=0.0037 (raw=0.0037 ema=0.0070) final=0.0052 agg=0.0047 *  (229s)
[ep 42/50] R=12 lr=1.40e-04 loss=3.6961e-03 val[raw]=0.0035 (raw=0.0035 ema=0.0068) final=0.0052 agg=0.0047 *  (238s)
[ep 43/50] R=12 lr=1.07e-04 loss=2.3317e-03 val[raw]=0.0034 (raw=0.0034 ema=0.0062) final=0.0047 agg=0.0043 *  (247s)
[ep 44/50] R=12 lr=7.94e-05 loss=2.2663e-03 val[raw]=0.0058 (raw=0.0058 ema=0.0066) final=0.0104 agg=0.0070  (255s)
[ep 45/50] R=12 lr=5.53e-05 loss=2.3799e-03 val[raw]=0.0032 (raw=0.0032 ema=0.0055) final=0.0045 agg=0.0042 *  (264s)
[ep 46/50] R=12 lr=3.55e-05 loss=1.9366e-03 val[raw]=0.0027 (raw=0.0027 ema=0.0048) final=0.0039 agg=0.0040 *  (273s)

Excellent! Now we're seeing major improvement. At epoch 46, raw=0.0027, final=0.0039, agg=0.0040. This is dramatically better than the baseline (0.0099 mean, 0.019 final). The jump to R=12 at epoch 37 combined with low LR really helped. Let me see the final epochs.

Major improvement , epoch 46 reaches raw=0.0027 mean, 0.0039 final (vs baseline 0.0099 / 0.019). The R=12 stage with low LR is very effective. Let me see the final result and evaluate on both splits.

bash

sleep 30; tail -6 /tmp/run1.log; echo "=== EVAL run1 ==="; cd /app && python3 eval_rollout.py /tmp/model_run1.pt 2>&1 | tail -8

output

[ep 44/50] R=12 lr=7.94e-05 loss=2.2663e-03 val[raw]=0.0058 (raw=0.0058 ema=0.0066) final=0.0104 agg=0.0070  (255s)
[ep 45/50] R=12 lr=5.53e-05 loss=2.3799e-03 val[raw]=0.0032 (raw=0.0032 ema=0.0055) final=0.0045 agg=0.0042 *  (264s)
[ep 46/50] R=12 lr=3.55e-05 loss=1.9366e-03 val[raw]=0.0027 (raw=0.0027 ema=0.0048) final=0.0039 agg=0.0040 *  (273s)
[ep 47/50] R=12 lr=2.00e-05 loss=1.7493e-03 val[raw]=0.0026 (raw=0.0026 ema=0.0047) final=0.0036 agg=0.0038 *  (282s)
[ep 48/50] R=12 lr=8.92e-06 loss=1.7042e-03 val[raw]=0.0026 (raw=0.0026 ema=0.0043) final=0.0036 agg=0.0038 *  (291s)
[ep 49/50] R=12 lr=2.23e-06 loss=1.6878e-03 val[raw]=0.0026 (raw=0.0026 ema=0.0039) final=0.0035 agg=0.0037 *  (300s)
=== EVAL run1 ===
[val] seed@4  rel_l2_mean=0.0026 agg=0.0037 final=0.0035 rmse=0.0044
[val] multi   rel_l2_mean=0.0022 agg=0.0034 final=0.0032
[val] per_step[0,1,2,4,9,19]= [0.002, 0.001, 0.002, 0.002, 0.002, 0.003]
[public_test] seed@4  rel_l2_mean=0.0022 agg=0.0036 final=0.0031 rmse=0.0045
[public_test] multi   rel_l2_mean=0.0017 agg=0.0028 final=0.0026
[public_test] per_step[0,1,2,4,9,19]= [0.001, 0.001, 0.001, 0.002, 0.002, 0.003]

This is a huge improvement. Let me compare: | Metric | Baseline | Improved (run1) | |---|---|---| | val rollout rel_l2_mean | 0.0099 | 0.0026 | | val final step | 0.0192 | 0.0035 | | val agg | 0.0146 | 0.0037 | | public_test mean | 0.0082 | 0.0022 | | public_test final | 0.0149 | 0.0031 | The per-step error is now almost flat (0.002 → 0.003 over 20 steps) instead of compounding (0.003 → 0.019). The rollout is now extremely stable. This is a ~4x improvement in rollout rel L2. Crucially, the per-step error barely grows now , the model learned to be stable under its own rollout. Now, the key question: does it generalize beyond horizon 20? The eval is exactly 20 steps, so I trained with R up to 12 and it generalizes to 20 well (per_step[19]=0.003). Let me see if I can push further. Ideas: 1. The run is clearly still improving at the end (LR→0). Could train longer or with a higher final-R. 2. The jump to R=12 at epoch 37 gave the biggest gains. Maybe reaching larger R earlier, or training more at R=12 (or even R=16/20), could help further. 3. Note raw consistently beats EMA here. The EMA warmup schedule converges to 0.999 decay which lags. I could lower EMA decay or just rely on raw. But keeping both and picking best is safe. Let me consider whether I'm at risk of overfitting to the horizon or to val. The public_test numbers are even better than val, so generalization is great. No overfitting concern. Let me think about what the grader actually scores: "rollout_rel_l2_score". Whatever monotone transform, lower rel L2 = higher score. We've driven it very low. Let me try to improve further with a refined run: - Extend the R=12 (or push to R=16) phase and train a bit longer (e.g., 60 epochs), reaching max R earlier so more epochs refine at large R. - Possibly start curriculum faster. But there's diminishing returns and risk. The current result is already excellent and robust across val + public_test. Let me do one more experiment to see if pushing R higher / longer helps, and keep whichever is best. I should guard against overfitting to my specific val start index; my eval already checks multiple starts (multi) and they're consistent. Let me also double check: is there any risk that training to such low error causes issues if the grader uses a different/longer horizon or different start? Per-step error is flat and low across all 20 steps and across multiple start points, so it's robust. Let me also test rollout stability beyond 20 steps (e.g., 40) to ensure it doesn't blow up , a sign of a genuinely stable model vs one overfit to 20. Let me test longer-horizon stability of run1 model.

This is a large improvement. Summary vs baseline: | Metric (val) | Baseline | Improved | |---|---|---| | rollout rel_l2 mean | 0.0099 | **0.0026** | | final step | 0.0192 | **0.0035** | | aggregate | 0.0146 | **0.0037** | The per-step error went from compounding (0.003→0.019) to nearly flat (0.002→0.003). Let me verify this reflects genuine stability (not horizon-20 overfitting) by rolling out much longer (40 steps), and check robustness across many start points.

bash

cd /app && python3 -c "
import json, torch
from eval_rollout import load_model, rollout_metrics
cfg=json.load(open('task_config.json')); dev='cuda'
m,fm,fs=load_model('/tmp/model_run1.pt',cfg,dev)
for split in ['val','public_test']:
    tr=torch.load(f'data/{split}/trajectories.pt',weights_only=True)['trajectories'].float()
    r20=rollout_metrics(m,tr,fm,fs,device=dev,starts=[4])
    r40=rollout_metrics(m,tr,fm,fs,device=dev,horizon=40,starts=[4])
    # many starts across the trajectory
    rmany=rollout_metrics(m,tr,fm,fs,device=dev,starts=list(range(4,70,4)))
    print(f'{split}: H20 mean={r20[\"rollout_rel_l2_mean\"]:.4f} | H40 mean={r40[\"rollout_rel_l2_mean\"]:.4f} final40={r40[\"final_step_rel_l2\"]:.4f} | many-start H20 mean={rmany[\"rollout_rel_l2_mean\"]:.4f}')
    print('   H40 per-step[4,9,19,29,39]=', [round(r40['per_step'][i],3) for i in [4,9,19,29,39]])
"

output

val: H20 mean=0.0026 | H40 mean=0.0046 final40=0.0089 | many-start H20 mean=0.0022
   H40 per-step[4,9,19,29,39]= [0.002, 0.002, 0.003, 0.006, 0.009]
public_test: H20 mean=0.0022 | H40 mean=0.0034 final40=0.0059 | many-start H20 mean=0.0017
   H40 per-step[4,9,19,29,39]= [0.002, 0.002, 0.003, 0.004, 0.006]

The model is genuinely stable , even at 40 steps (double the eval horizon), the error only grows to 0.009 (val) / 0.006 (public_test). And it's robust across many start points (many-start H20 mean 0.0022, consistent). This confirms the model learned stable dynamics rather than overfitting to horizon 20. This is a strong, robust result. Now let me see if I can squeeze out more. Let me try a refined configuration: - Reach R=12 earlier and possibly go to R=16, train 60 epochs. - This gives more refinement time at large R. Let me run a second experiment and compare. I'll keep run1 as the current best and only replace /app/outputs if run2 is clearly better AND robust. Let me design run2: - EPOCHS=64 - CURR: "0:1,2:2,4:4,8:6,13:8,19:10,26:12,38:16" , reach larger R, more time at high R - Maybe slightly lower LR peak (1.5e-3) to reduce the mid-training instability I saw. Actually the instability didn't hurt final result. Keep 2e-3 but it's fine. Let me keep 2e-3. Actually, let me reconsider. The biggest gains came after reaching R=12 with low LR. Training at R=16 might help horizon-20 generalization even more. But R=16 BPTT is heavier. Still cheap on H100 (~12s/epoch). Let me try. Let me also consider: the final LR went to 0 which "locks in" the raw weights. Good. Let me launch run2. One concern: time. run1 took 300s for 50 epochs. run2 with higher R and 64 epochs maybe ~450-500s. That exceeds single-command 300s limit, so I'll background it. Fine. Let me launch run2.

Excellent , the model is genuinely stable: even at **40 steps** (2× the eval horizon) error only reaches 0.009, and it's consistent across many start points. This confirms it learned stable dynamics rather than overfitting to horizon 20. Let me try one refinement run reaching larger rollout lengths (R up to 16) with more epochs, to see if I can squeeze out more, while keeping run1 safe as the current best.

bash

cd /app && cp /tmp/model_run1.pt /app/outputs/model.pt && ls -la /app/outputs/ && echo "saved run1 as current best" && rm -f /tmp/run2.log && nohup env EPOCHS=64 WARMUP=3 LR=2e-3 \
  CURR="0:1,2:2,4:4,8:6,13:8,19:10,26:12,40:16" \
  SAVE=/tmp/model_run2.pt python3 train_improved.py > /tmp/run2.log 2>&1 &
echo "launched run2 pid $!"

output

launched run2 pid 290

I've saved run1 as the current best in /app/outputs/model.pt. Now run2 is training. Let me verify the saved model.pt is valid (loadable with weights_only=True, correct contract) while run2 trains.

While run2 trains, let me verify the currently-saved `/app/outputs/model.pt` strictly satisfies the output contract (loadable with `weights_only=True`, correct keys/types, matches `build_model`).

bash

cd /app && python3 -c "
import json, torch
from candidate_model import build_model
cfg=json.load(open('task_config.json'))
d=torch.load('/app/outputs/model.pt', weights_only=True)
assert set(['state_dict','feat_mean','feat_std']).issubset(d.keys()), d.keys()
assert isinstance(d['feat_mean'],float) and isinstance(d['feat_std'],float), (type(d['feat_mean']),type(d['feat_std']))
m=build_model(cfg)
missing,unexpected=m.load_state_dict(d['state_dict'],strict=True)  # will raise if mismatch except returns empty lists
print('contract OK: keys=',list(d.keys()))
print('feat_mean=%.6g feat_std=%.6g'%(d['feat_mean'],d['feat_std']))
print('num params loaded:', sum(v.numel() for v in d['state_dict'].values()))
# forward works
x=torch.randn(2,4,128); print('forward out', tuple(m(x).shape))
"

output

contract OK: keys= ['state_dict', 'feat_mean', 'feat_std']
feat_mean=-3.81982e-09 feat_std=1.15997
num params loaded: 71041
forward out (2, 1, 128)

The contract is satisfied. feat_mean ≈ 0, feat_std ≈ 1.16 (matches the data std I computed earlier). Now let me wait for run2 to progress and compare. Let me poll.

Contract verified: loads with `weights_only=True`, has the right keys/types, matches `build_model`, and forward works. Now let me monitor run2's progress.

bash

sleep 150 && grep -E "ep (1|5|10|15|20|25|30|35)/" /tmp/run2.log | tail -20

output

[ep 1/64] R=1 lr=6.72e-04 loss=5.7708e-01 val[raw]=0.1434 (raw=0.1434 ema=0.1588) final=0.2487 agg=0.1960 *  (4s)
[ep 5/64] R=4 lr=1.99e-03 loss=2.7840e-02 val[ema]=0.0574 (raw=0.0663 ema=0.0574) final=0.1003 agg=0.0674 *  (17s)
[ep 10/64] R=6 lr=1.94e-03 loss=2.7747e-02 val[ema]=0.0956 (raw=0.1120 ema=0.0956) final=0.1775 agg=0.1142  (39s)
[ep 15/64] R=8 lr=1.82e-03 loss=2.6513e-02 val[raw]=0.0965 (raw=0.0965 ema=0.1014) final=0.2098 agg=0.1067  (67s)
[ep 20/64] R=10 lr=1.64e-03 loss=1.5505e-02 val[raw]=0.0243 (raw=0.0243 ema=0.0270) final=0.0421 agg=0.0286  (101s)
[ep 25/64] R=10 lr=1.42e-03 loss=1.7697e-02 val[raw]=0.0211 (raw=0.0211 ema=0.0606) final=0.0343 agg=0.0238 *  (138s)

run2 is progressing similarly. At epoch 25 (R=10, LR 1.42e-3) raw=0.0211, best so far. It's tracking similarly to run1 at comparable stages. The big gains come later as LR decays and R increases to 12 then 16. Let me wait for it to reach the later epochs. Let me wait more.

bash

sleep 150 && grep -E "ep (30|35|40|45|48|50|52|54)/" /tmp/run2.log | tail -20

output

[ep 30/64] R=12 lr=1.18e-03 loss=1.3415e-02 val[raw]=0.0131 (raw=0.0131 ema=0.0402) final=0.0186 agg=0.0134 *  (179s)
[ep 35/64] R=12 lr=9.23e-04 loss=7.9879e-03 val[raw]=0.0107 (raw=0.0107 ema=0.0164) final=0.0152 agg=0.0116  (219s)
[ep 40/64] R=12 lr=6.71e-04 loss=6.4531e-03 val[raw]=0.0094 (raw=0.0094 ema=0.0114) final=0.0141 agg=0.0106  (256s)
[ep 45/64] R=16 lr=4.42e-04 loss=5.1915e-03 val[raw]=0.0054 (raw=0.0054 ema=0.0102) final=0.0075 agg=0.0067 *  (303s)

run2 at epoch 45 (R=16, LR 4.4e-4) raw=0.0054 and improving. Comparable to run1 at similar LR. The final epochs with LR→0 will refine further. Let me wait for completion (~64 epochs, should be ~400s total, we're at 303s for ep45, so maybe another ~120s).

bash

sleep 130 && tail -7 /tmp/run2.log && echo "=== EVAL run2 ===" && cd /app && python3 eval_rollout.py /tmp/model_run2.pt 2>&1 | tail -6

output

[ep 53/64] R=16 lr=1.56e-04 loss=3.4793e-03 val[raw]=0.0035 (raw=0.0035 ema=0.0059) final=0.0057 agg=0.0049 *  (382s)
[ep 54/64] R=16 lr=1.30e-04 loss=4.1335e-03 val[raw]=0.0034 (raw=0.0034 ema=0.0064) final=0.0051 agg=0.0044 *  (392s)
[ep 55/64] R=16 lr=1.06e-04 loss=3.3843e-03 val[raw]=0.0035 (raw=0.0035 ema=0.0062) final=0.0054 agg=0.0044  (401s)
[ep 56/64] R=16 lr=8.37e-05 loss=2.6385e-03 val[raw]=0.0037 (raw=0.0037 ema=0.0057) final=0.0061 agg=0.0047  (410s)
[ep 57/64] R=16 lr=6.43e-05 loss=2.3907e-03 val[raw]=0.0028 (raw=0.0028 ema=0.0052) final=0.0036 agg=0.0038 *  (419s)
[ep 58/64] R=16 lr=4.74e-05 loss=1.9902e-03 val[raw]=0.0025 (raw=0.0025 ema=0.0048) final=0.0030 agg=0.0036 *  (429s)
[ep 59/64] R=16 lr=3.30e-05 loss=1.8827e-03 val[raw]=0.0025 (raw=0.0025 ema=0.0047) final=0.0031 agg=0.0036 *  (438s)
=== EVAL run2 ===
    return _open_file(name_or_buffer, mode)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.11/site-packages/torch/serialization.py", line 640, in __init__
    super().__init__(open(name, mode))
                     ^^^^^^^^^^^^^^^^
FileNotFoundError: [Errno 2] No such file or directory: '/tmp/model_run2.pt'

run2 is still running (epoch 59/64), so the file hasn't been saved yet. Let me wait for it to finish, then evaluate.

Run2 is still on epoch 59 (saves only at the end). Let me wait for it to finish, then evaluate.

bash

sleep 60 && tail -4 /tmp/run2.log && echo "=== EVAL run2 ===" && cd /app && python3 eval_rollout.py /tmp/model_run2.pt 2>&1 | tail -6

output

[ep 62/64] R=16 lr=5.30e-06 loss=1.7635e-03 val[raw]=0.0024 (raw=0.0024 ema=0.0038) final=0.0028 agg=0.0036 *  (466s)
[ep 63/64] R=16 lr=1.33e-06 loss=1.7533e-03 val[raw]=0.0024 (raw=0.0024 ema=0.0036) final=0.0028 agg=0.0035 *  (476s)
[ep 64/64] R=16 lr=0.00e+00 loss=1.7475e-03 val[raw]=0.0024 (raw=0.0024 ema=0.0033) final=0.0028 agg=0.0036  (486s)
[done] best val_roll_mean=0.0024 -> wrote /tmp/model_run2.pt
=== EVAL run2 ===
[val] seed@4  rel_l2_mean=0.0024 agg=0.0035 final=0.0028 rmse=0.0042
[val] multi   rel_l2_mean=0.0021 agg=0.0032 final=0.0028
[val] per_step[0,1,2,4,9,19]= [0.002, 0.002, 0.002, 0.002, 0.002, 0.003]
[public_test] seed@4  rel_l2_mean=0.0017 agg=0.0023 final=0.0021 rmse=0.0029
[public_test] multi   rel_l2_mean=0.0016 agg=0.0028 final=0.0024
[public_test] per_step[0,1,2,4,9,19]= [0.001, 0.001, 0.001, 0.001, 0.002, 0.002]

run2 is slightly better than run1: | Metric | run1 | run2 | |---|---|---| | val mean | 0.0026 | 0.0024 | | val final | 0.0035 | 0.0028 | | val agg | 0.0037 | 0.0035 | | public_test mean | 0.0022 | 0.0017 | | public_test final | 0.0031 | 0.0021 | run2 is modestly better, especially on final step and public_test. The larger R (16) and more epochs helped the final-step accuracy. Let me verify long-horizon stability of run2 as well, then adopt it. Let me check run2's H40 stability and robustness.

Run2 improved further, especially on final-step and public_test (public_test mean 0.0017 vs 0.0022). Let me confirm run2 is equally robust at long horizons and many start points before adopting it.

bash

cd /app && python3 -c "
import json, torch
from eval_rollout import load_model, rollout_metrics
cfg=json.load(open('task_config.json')); dev='cuda'
for tag,path in [('run1','/tmp/model_run1.pt'),('run2','/tmp/model_run2.pt')]:
    m,fm,fs=load_model(path,cfg,dev)
    print('==',tag,'==')
    for split in ['val','public_test']:
        tr=torch.load(f'data/{split}/trajectories.pt',weights_only=True)['trajectories'].float()
        r20=rollout_metrics(m,tr,fm,fs,device=dev,starts=[4])
        r40=rollout_metrics(m,tr,fm,fs,device=dev,horizon=40,starts=[4])
        rmany=rollout_metrics(m,tr,fm,fs,device=dev,starts=list(range(4,76,2)))
        print(f'  {split}: H20={r20[\"rollout_rel_l2_mean\"]:.4f} final20={r20[\"final_step_rel_l2\"]:.4f} | H40={r40[\"rollout_rel_l2_mean\"]:.4f} final40={r40[\"final_step_rel_l2\"]:.4f} | many-start H20={rmany[\"rollout_rel_l2_mean\"]:.4f}')
"

output

== run1 ==
  val: H20=0.0026 final20=0.0035 | H40=0.0046 final40=0.0089 | many-start H20=0.0022
  public_test: H20=0.0022 final20=0.0031 | H40=0.0034 final40=0.0059 | many-start H20=0.0017
== run2 ==
  val: H20=0.0024 final20=0.0028 | H40=0.0031 final40=0.0050 | many-start H20=0.0020
  public_test: H20=0.0017 final20=0.0021 | H40=0.0026 final40=0.0050 | many-start H20=0.0016

run2 dominates run1 on every metric, including long-horizon (H40) and many-start robustness: - val H40: 0.0031 (run2) vs 0.0046 (run1) - val final40: 0.0050 vs 0.0089 So run2 is strictly better and more stable. Let me adopt run2 as the final model. Now, should I try to push even further? The gains from run1→run2 were modest. I could try: - Even longer training / R up to 20. - Ensemble? No, single model required (state_dict matching build_model). - Multiple seeds and pick best on val. But I'm wary of overfitting to my val metric. run2 generalizes well to public_test (even better than val), so it's not overfitting. The result is already ~4-5x better than baseline and extremely stable. Let me consider one more improvement attempt: a run that reaches R=20 (matching eval horizon exactly) to see if final-step accuracy improves further. But R=20 BPTT is heavier and gains are likely marginal. Given diminishing returns and the risk/time, let me do one more modest experiment with a different seed to check variance and possibly get a slightly better model, reaching R=18-20. Actually, let me reconsider time budget , each run ~8 min. I have budget for one or two more. Actually, a cleaner test: does pushing to R=20 help? Let me try run3 with curriculum reaching R=20, and a couple more epochs at the top. But there's a subtlety: with R=20 and k=4, I need sequences of length 24, so starts range over T-24+1 = 77 positions per trajectory, still 200*77=15400 sequences. Fine. But honestly, I worry that very large R with full BPTT and the tiny model might not improve much and could even slightly hurt due to optimization difficulty. run2 at R=16 already generalizes to H40 superbly. Let me make a decision: I'll adopt run2 now (copy to outputs) so I have a strong checkpoint locked in. Then I'll attempt run3 (reach R=20, maybe different seed, slightly more epochs) and only replace if clearly better and robust. Let me copy run2 to outputs first.

Run2 strictly dominates run1 on every metric including long-horizon H40 and many-start robustness. I'll lock in run2 as the current best, then attempt one more run reaching R=20 (matching the exact eval horizon) to see if it helps.

bash

cd /app && cp /tmp/model_run2.pt /app/outputs/model.pt && echo "adopted run2 as current best" && rm -f /tmp/run3.log && nohup env SEED=12345 EPOCHS=70 WARMUP=3 LR=2e-3 \
  CURR="0:1,2:2,4:4,8:6,13:8,19:10,26:12,36:16,52:20" \
  SAVE=/tmp/model_run3.pt python3 train_improved.py > /tmp/run3.log 2>&1 &
echo "launched run3 pid $!"

output

launched run3 pid 379

While run3 trains (~9-10 min), let me reflect on whether there are any other improvements or risks I should address. 1. **Robustness to grader's exact rollout implementation**: The grader uses the model output directly. My model predicts normalized next frame, and I denormalize. The grader will use feat_mean/feat_std to normalize input and denormalize output exactly as in my eval harness (that's the standard contract). My eval_rollout mimics this. Good. 2. **What if the grader computes rel L2 in a slightly different way** (e.g., aggregate vs per-step)? I report both agg and mean; both are excellent. Final-step also excellent. So any reasonable scoring will be near-optimal. 3. **Determinism/seed**: Not required for the checkpoint. 4. **Make sure the final model.pt always has the best weights** , I pick best across epochs by val rollout mean. Good. Let me also consider: could I improve the training script's robustness by also selecting the best checkpoint based on a combined metric (mean + final) rather than just mean? Currently I select on rollout_rel_l2_mean. The final-step is a secondary metric. Selecting on mean is fine since mean and final are correlated. run2's selected model had great final too. Let me think about whether there's any subtle bug in my rollout eval that could make my numbers look better than reality. In rollout_metrics, I seed with ground-truth normalized frames [s-k:s], then iteratively predict. That's exactly autoregressive. The comparison denormalizes. Looks correct. The baseline got 0.0099 which matched the single-step val_rel_l2 trend (single step 0.003 at step1). Consistent. Good. One more: the grader might feed the window in un-normalized form and expect the model to handle normalization internally? No , the contract explicitly provides feat_mean/feat_std as separate scalars, meaning the grader normalizes inputs and denormalizes outputs using them (the model itself operates in normalized space, as in train.py). My setup matches train.py's convention exactly. Good. Let me wait and monitor run3. Meanwhile, let me also write a short summary/README of what I did, and clean up. Actually, let me hold the summary until final. Let me also double-check: the task says "You may change loss, optimizer, scheduler, augmentation, EMA, epochs." All within allowed scope. I did not change the architecture or the model config (width/modes/layers fixed). Good. I did change the training recipe substantially. The checkpoint is compatible with build_model(config). Good. Let me monitor run3 progress.

While run3 trains, let me reason about robustness to the grader's exact implementation. My `eval_rollout.py` mirrors the standard contract (normalize input with `feat_mean/feat_std`, model operates in normalized space, denormalize output) , the same convention as the provided `train.py`. I report both per-step-mean and aggregate rel-L2 plus final-step, and all are strong, so any reasonable scoring variant will score well. Let me check run3's progress.

bash

sleep 180 && grep -E "ep (1|10|20|30|36|40|45)/" /tmp/run3.log | tail -20

output

[ep 1/70] R=1 lr=6.72e-04 loss=6.1334e-01 val[raw]=0.1436 (raw=0.1436 ema=0.1740) final=0.2385 agg=0.1986 *  (2s)
[ep 10/70] R=6 lr=1.95e-03 loss=3.8792e-02 val[raw]=0.0808 (raw=0.0808 ema=0.1234) final=0.1063 agg=0.0989  (25s)
[ep 20/70] R=10 lr=1.70e-03 loss=3.3530e-02 val[raw]=0.0388 (raw=0.0388 ema=0.0539) final=0.0608 agg=0.0465  (71s)
[ep 30/70] R=12 lr=1.30e-03 loss=1.0309e-02 val[ema]=0.0136 (raw=0.0137 ema=0.0136) final=0.0202 agg=0.0173 *  (137s)
[ep 36/70] R=12 lr=1.02e-03 loss=8.5006e-03 val[raw]=0.0110 (raw=0.0110 ema=0.0121) final=0.0165 agg=0.0124 *  (179s)

run3 is tracking similarly. Let me wait for the later epochs where R=16 (epoch 36) and R=20 (epoch 52) kick in with low LR. That's where the gains materialize. Let me wait.

bash

sleep 200 && grep -E "ep (45|52|55|60|62)/" /tmp/run3.log | tail -20

output

[ep 45/70] R=16 lr=6.12e-04 loss=1.1964e-02 val[ema]=0.0283 (raw=0.0306 ema=0.0283) final=0.0500 agg=0.0326  (262s)
[ep 52/70] R=16 lr=3.36e-04 loss=4.1980e-03 val[raw]=0.0051 (raw=0.0051 ema=0.0070) final=0.0075 agg=0.0066 *  (328s)
[ep 55/70] R=20 lr=2.37e-04 loss=5.3016e-03 val[raw]=0.0049 (raw=0.0049 ema=0.0050) final=0.0073 agg=0.0063 *  (362s)

run3 reached R=20 at epoch 52. At epoch 55 (R=20, LR 2.37e-4) raw=0.0049, improving. The last ~15 epochs with decaying LR will refine. Let me wait for completion (70 epochs, currently ep55 at 362s, ~15 more epochs at ~12s each = ~180s).

Run3 has reached the R=20 stage (matching the exact eval horizon). Let me wait for it to finish and then compare all three.

bash

sleep 200 && tail -4 /tmp/run3.log && echo "=== EVAL run3 ===" && cd /app && python3 eval_rollout.py /tmp/model_run3.pt 2>&1 | tail -6

output

[ep 68/70] R=20 lr=4.39e-06 loss=2.2045e-03 val[raw]=0.0029 (raw=0.0029 ema=0.0035) final=0.0039 agg=0.0044 *  (515s)
[ep 69/70] R=20 lr=1.10e-06 loss=2.1879e-03 val[raw]=0.0029 (raw=0.0029 ema=0.0034) final=0.0039 agg=0.0044 *  (526s)
[ep 70/70] R=20 lr=0.00e+00 loss=2.1873e-03 val[raw]=0.0029 (raw=0.0029 ema=0.0033) final=0.0039 agg=0.0044 *  (538s)
[done] best val_roll_mean=0.0029 -> wrote /tmp/model_run3.pt
=== EVAL run3 ===
[val] seed@4  rel_l2_mean=0.0029 agg=0.0044 final=0.0039 rmse=0.0052
[val] multi   rel_l2_mean=0.0024 agg=0.0037 final=0.0033
[val] per_step[0,1,2,4,9,19]= [0.003, 0.002, 0.002, 0.002, 0.003, 0.004]
[public_test] seed@4  rel_l2_mean=0.0022 agg=0.0039 final=0.0024 rmse=0.0049
[public_test] multi   rel_l2_mean=0.0019 agg=0.0039 final=0.0026
[public_test] per_step[0,1,2,4,9,19]= [0.002, 0.001, 0.001, 0.002, 0.002, 0.002]

Interesting , run3 (R up to 20, different seed 12345) is actually slightly WORSE than run2: - val mean: run3=0.0029 vs run2=0.0024 - val final: run3=0.0039 vs run2=0.0028 - public_test mean: run3=0.0022 vs run2=0.0017 So pushing to R=20 did not help; run2 (R=16, seed default) remains the best. This is likely a combination of the different seed and the fact that R=20 BPTT is harder to optimize with the tiny model (more steps to backprop, each epoch has fewer refinement iterations at the top R since it only starts at epoch 52). The R=16 config with more epochs at the top stage (run2 had epochs 40-63 at R=16) gave better convergence. So run2 stays as the best. It's already in /app/outputs/model.pt. Good. Let me make sure I keep run2 as the final. Let me reconfirm /app/outputs/model.pt is run2 and re-verify contract + final eval once more. Also, given run variance (seed matters), let me consider doing one more run with the run2 recipe but the default seed to confirm run2 wasn't a lucky outlier , actually run2 IS already the default-seed run2 recipe. Should I try to beat run2 with a run that mimics run2 exactly (R up to 16) but trains even longer at R=16, or tweaks? The improvement from run1→run2 came from (a) reaching higher R (16) and (b) more epochs. run3 shows R=20 doesn't help and seed variance exists. Let me try one more targeted run: run2 recipe but extend total epochs so there's even more refinement time at R=16 with very low LR, same default seed. E.g., EPOCHS=80, same curriculum reaching R=16 at epoch 40, so 40 epochs at R=16. This might edge out run2. But diminishing returns. Let me try it since I have budget, and keep run2 if it doesn't beat it. Actually, let me reconsider. run2 had curriculum "0:1,2:2,4:4,8:6,13:8,19:10,26:12,40:16" with 64 epochs → 24 epochs at R=16 (epochs 40-63). The last ~10 epochs (LR < 1e-4) gave the refinement to 0.0024. Let me try run4: same as run2 but 80 epochs and reach R=16 a bit earlier (epoch 34), giving more low-LR refinement at R=16. Keep default seed. Let me see if it beats 0.0024/0.0028. Actually, I realize the variance between runs (~0.0024 to 0.0029) is on the order of the improvements I'm chasing. I should be careful not to overfit to val noise. Let me instead verify which model is genuinely best using a more robust evaluation: average over many start points AND both val+public_test, which reduces noise. By that measure run2 was best (val many-start 0.0020, public_test many-start 0.0016). Let me do one more run (run4) with the run2 recipe, default seed, slightly more epochs at R=16, and compare robustly. If it doesn't clearly beat run2, I keep run2. Let me launch run4.

Run3 (R up to 20, different seed) came out slightly **worse** than run2 , pushing to R=20 didn't help, and there's some seed variance. Run2 (R≤16) remains best and is already saved. Let me try one more run with run2's recipe but more refinement time at the top R=16 stage (default seed), and compare robustly.

bash

cd /app && rm -f /tmp/run4.log && nohup env EPOCHS=80 WARMUP=3 LR=2e-3 \
  CURR="0:1,2:2,4:4,7:6,11:8,16:10,22:12,30:16" \
  SAVE=/tmp/model_run4.pt python3 train_improved.py > /tmp/run4.log 2>&1 &
echo "launched run4 pid $!"

output

launched run4 pid 430

While run4 trains (~10 min for 80 epochs at R=16), let me think about whether there's anything else to improve or verify. Let me reconsider the overall approach once more and ensure the final deliverable is solid regardless of run4's outcome. run2 is safely in /app/outputs/model.pt. Let me use this time to write a clean, consolidated final training script that reproduces the best recipe (run2-style) as the canonical train.py-compatible artifact, and a brief README documenting the approach. Actually, the task just requires the checkpoint at /app/outputs/model.pt. But providing a clean training script is good practice. My train_improved.py already does this. Let me also reconsider: the task says "Save your checkpoint to /app/outputs/model.pt." and the output contract. I've satisfied that. Let me also make sure there are no issues with the grader possibly using torch.load weights_only=True on a dict containing Python floats , that's fine (floats are allowed). state_dict of tensors is fine. Good. Let me wait for run4 and compare. Let me monitor periodically. I'll check at key milestones. Actually, let me reconsider whether a longer R=16 phase (run4) will help. run4 reaches R=16 at epoch 30, with 80 total epochs → 50 epochs at R=16, with a long low-LR tail. This should refine well. Let me see. Let me wait.

While run4 trains, let me do a robust head-to-head comparison of the candidates so far using averaging over many start points across both splits (which reduces evaluation noise), confirming run2 is the right choice to keep.

bash

cd /app && python3 -c "
import json, torch
from eval_rollout import load_model, rollout_metrics
cfg=json.load(open('task_config.json')); dev='cuda'
starts=list(range(4,76,2))
for tag,path in [('run1','/tmp/model_run1.pt'),('run2','/tmp/model_run2.pt'),('run3','/tmp/model_run3.pt')]:
    m,fm,fs=load_model(path,cfg,dev)
    tot=[]
    for split in ['val','public_test']:
        tr=torch.load(f'data/{split}/trajectories.pt',weights_only=True)['trajectories'].float()
        r=rollout_metrics(m,tr,fm,fs,device=dev,starts=starts)
        tot.append(r['rollout_rel_l2_mean'])
    print(f'{tag}: robust many-start  val={tot[0]:.4f}  public_test={tot[1]:.4f}  avg={sum(tot)/2:.4f}')
"

output

run1: robust many-start  val=0.0022  public_test=0.0017  avg=0.0020
run2: robust many-start  val=0.0020  public_test=0.0016  avg=0.0018
run3: robust many-start  val=0.0024  public_test=0.0019  avg=0.0021

Robust many-start comparison confirms run2 is best (avg 0.0018 vs run1 0.0020, run3 0.0021). run2 is safely in /app/outputs. Let me wait for run4 and compare. Let me monitor.

The robust many-start comparison confirms run2 is best (avg 0.0018). It's safely saved. Let me check run4's progress in the late stages.

bash

sleep 240 && grep -E "ep (30|40|50|55|60)/" /tmp/run4.log | tail -20

output

[ep 30/80] R=12 lr=1.45e-03 loss=1.7574e-02 val[raw]=0.0167 (raw=0.0167 ema=0.0608) final=0.0249 agg=0.0171 *  (169s)

Hmm, the curriculum says "30:16" meaning at epoch index 30 (0-based) R becomes 16. But the log shows "ep 30/80 R=12". Note the log prints epoch+1, so "ep 30" = epoch index 29, which is still R=12 (R=16 starts at index 30 = "ep 31"). OK that's consistent. Only one line matched because grep pattern matched ep 30 only so far; the run is around epoch 30. It's slower now at R=16. Let me wait more. Actually wait, only "ep 30" showed. The run is at ~epoch 30 after 240s+. At R=16 each epoch is ~12s, so 50 more epochs = ~600s = 10 min. Let me wait in chunks.

Run4 is at epoch 30 (R=16 stage beginning). The R=16 epochs take longer. Let me wait for more progress.

bash

sleep 260 && grep -E "ep (40|50|55|60|62)/" /tmp/run4.log | tail -20

output

[ep 40/80] R=16 lr=1.06e-03 loss=2.0683e-02 val[raw]=0.0424 (raw=0.0424 ema=0.0468) final=0.0789 agg=0.0529  (266s)
[ep 50/80] R=16 lr=6.60e-04 loss=6.6183e-03 val[raw]=0.0076 (raw=0.0076 ema=0.0112) final=0.0105 agg=0.0080 *  (357s)
[ep 55/80] R=16 lr=4.77e-04 loss=5.4929e-03 val[raw]=0.0073 (raw=0.0073 ema=0.0092) final=0.0146 agg=0.0082  (403s)
[ep 60/80] R=16 lr=3.15e-04 loss=3.6657e-03 val[raw]=0.0044 (raw=0.0044 ema=0.0070) final=0.0066 agg=0.0052 *  (445s)
[ep 62/80] R=16 lr=2.58e-04 loss=3.2130e-03 val[raw]=0.0038 (raw=0.0038 ema=0.0065) final=0.0048 agg=0.0044 *  (463s)

run4 at epoch 62 (R=16, LR 2.58e-4) raw=0.0038, still has ~18 epochs of low-LR refinement left. It's tracking similarly to run2 at comparable LR (run2 at epoch 53, LR 1.56e-4, was 0.0035). The final tail will determine if it beats run2's 0.0024. Let me wait for completion.

Run4 is tracking similarly to run2 with ~18 low-LR epochs left for refinement. Let me wait for it to finish and compare.

bash

sleep 200 && tail -4 /tmp/run4.log && echo "=== EVAL run4 ===" && cd /app && python3 eval_rollout.py /tmp/model_run4.pt 2>&1 | tail -6

output

[ep 78/80] R=16 lr=3.33e-06 loss=1.5742e-03 val[raw]=0.0023 (raw=0.0023 ema=0.0035) final=0.0031 agg=0.0034 *  (620s)
[ep 79/80] R=16 lr=8.32e-07 loss=1.5656e-03 val[raw]=0.0023 (raw=0.0023 ema=0.0033) final=0.0030 agg=0.0034 *  (631s)
[ep 80/80] R=16 lr=0.00e+00 loss=1.5635e-03 val[raw]=0.0023 (raw=0.0023 ema=0.0031) final=0.0030 agg=0.0035  (641s)
[done] best val_roll_mean=0.0023 -> wrote /tmp/model_run4.pt
=== EVAL run4 ===
[val] seed@4  rel_l2_mean=0.0023 agg=0.0034 final=0.0030 rmse=0.0041
[val] multi   rel_l2_mean=0.0019 agg=0.0030 final=0.0028
[val] per_step[0,1,2,4,9,19]= [0.002, 0.002, 0.002, 0.002, 0.002, 0.003]
[public_test] seed@4  rel_l2_mean=0.0016 agg=0.0023 final=0.0020 rmse=0.0029
[public_test] multi   rel_l2_mean=0.0015 agg=0.0026 final=0.0021
[public_test] per_step[0,1,2,4,9,19]= [0.001, 0.001, 0.001, 0.001, 0.002, 0.002]

run4 is marginally better than run2: - val mean: run4=0.0023 vs run2=0.0024 - val final: run4=0.0030 vs run2=0.0028 (slightly worse) - public_test mean: run4=0.0016 vs run2=0.0017 Very close. Let me do the robust many-start comparison between run2 and run4 to decide.

Run4 is essentially tied with run2 (val mean 0.0023 vs 0.0024). Let me do the robust many-start comparison to pick the better of the two definitively.

bash

cd /app && python3 -c "
import json, torch
from eval_rollout import load_model, rollout_metrics
cfg=json.load(open('task_config.json')); dev='cuda'
starts=list(range(4,76,2))
res={}
for tag,path in [('run2','/tmp/model_run2.pt'),('run4','/tmp/model_run4.pt')]:
    m,fm,fs=load_model(path,cfg,dev)
    tot=[]; fin=[]
    for split in ['val','public_test']:
        tr=torch.load(f'data/{split}/trajectories.pt',weights_only=True)['trajectories'].float()
        r=rollout_metrics(m,tr,fm,fs,device=dev,starts=starts)
        tot.append(r['rollout_rel_l2_mean']); fin.append(r['final_step_rel_l2'])
    res[tag]=(sum(tot)/2, sum(fin)/2)
    print(f'{tag}: many-start mean avg={sum(tot)/2:.5f}  final avg={sum(fin)/2:.5f}  (val={tot[0]:.4f},pt={tot[1]:.4f})')
print('winner(mean):', min(res, key=lambda k: res[k][0]))
"

output

run2: many-start mean avg=0.00183  final avg=0.00268  (val=0.0020,pt=0.0016)
run4: many-start mean avg=0.00167  final avg=0.00251  (val=0.0019,pt=0.0015)
winner(mean): run4

run4 is marginally better on the robust many-start metric (0.00167 vs 0.00183 mean, and better final too). The difference is small but consistent across val and public_test, and on both mean and final. So run4 is the best model. Let me adopt run4. Both run2 and run4 use the same recipe/seed; run4 just had more epochs and reached R=16 earlier, giving more refinement. The consistent improvement across val+public_test on both mean and final metrics suggests it's a real (if small) gain, not noise. Let me adopt run4 as the final model. Let me copy run4 to /app/outputs/model.pt and do a final verification of the contract and metrics.

Run4 is consistently (if marginally) better across both splits on both mean and final-step metrics , a real gain from more refinement time at R=16. I'll adopt run4 as the final model and do a complete verification.

bash

cd /app && cp /tmp/model_run4.pt /app/outputs/model.pt && echo "=== FINAL contract + forward check ===" && python3 -c "
import json, torch
from candidate_model import build_model
cfg=json.load(open('task_config.json'))
d=torch.load('/app/outputs/model.pt', weights_only=True)
assert set(d.keys())>= {'state_dict','feat_mean','feat_std'}
assert isinstance(d['feat_mean'],float) and isinstance(d['feat_std'],float)
m=build_model(cfg); m.load_state_dict(d['state_dict'], strict=True); m.eval()
print('keys:', sorted(d.keys()))
print('feat_mean=%.6g feat_std=%.6g'%(d['feat_mean'],d['feat_std']))
import torch as t
with t.no_grad():
    y=m(t.randn(3,4,128))
print('forward [3,4,128]->',tuple(y.shape),'finite:',bool(t.isfinite(y).all()))
print('CONTRACT OK')
"

output

=== FINAL contract + forward check ===
keys: ['feat_mean', 'feat_std', 'state_dict']
feat_mean=-3.81982e-09 feat_std=1.15997
forward [3,4,128]-> (3, 1, 128) finite: True
CONTRACT OK

Now let me run the final, definitive evaluation of the adopted model versus the original baseline, mimicking the grader's 20-step rollout, to document the full improvement.

bash

cd /app && echo "=== FINAL MODEL (/app/outputs/model.pt) , grader-style 20-step rollout ===" && python3 eval_rollout.py /app/outputs/model.pt 2>&1 | tail -6

output

=== FINAL MODEL (/app/outputs/model.pt) , grader-style 20-step rollout ===
[val] seed@4  rel_l2_mean=0.0023 agg=0.0034 final=0.0030 rmse=0.0041
[val] multi   rel_l2_mean=0.0019 agg=0.0030 final=0.0028
[val] per_step[0,1,2,4,9,19]= [0.002, 0.002, 0.002, 0.002, 0.002, 0.003]
[public_test] seed@4  rel_l2_mean=0.0016 agg=0.0023 final=0.0020 rmse=0.0029
[public_test] multi   rel_l2_mean=0.0015 agg=0.0026 final=0.0021
[public_test] per_step[0,1,2,4,9,19]= [0.001, 0.001, 0.001, 0.001, 0.002, 0.002]

The final model is excellent. Let me produce a clear side-by-side summary of baseline vs final to document the improvement. Let me retrain the baseline quickly (it's only 40s) to have a clean comparison, or I can just reuse the numbers I measured earlier. I already have baseline numbers from earlier: Baseline (10 epochs, the provided train.py): - val seed@4: rel_l2_mean=0.0099, agg=0.0146, final=0.0192, rmse=0.0171 - public_test seed@4: rel_l2_mean=0.0082, agg=0.0148, final=0.0149, rmse=0.0186 Final model (run4): - val seed@4: rel_l2_mean=0.0023, agg=0.0034, final=0.0030, rmse=0.0041 - public_test seed@4: rel_l2_mean=0.0016, agg=0.0023, final=0.0020, rmse=0.0029 Improvements: - val rollout rel_l2_mean: 0.0099 → 0.0023 (4.3x better) - val final step: 0.0192 → 0.0030 (6.4x better) - public_test mean: 0.0082 → 0.0016 (5.1x better) - public_test final: 0.0149 → 0.0020 (7.5x better) The final-step improvement is the most dramatic because the baseline's error compounds over the rollout while mine stays flat. Let me make a nice summary table by running a single script that evaluates both the baseline checkpoint (if I still have it) and the final. Actually I overwrote /app/outputs/model.pt with my runs. Let me just regenerate the baseline to /tmp and produce a clean comparison table. It's quick (40s). Let me regenerate the baseline into /tmp and produce a final comparison table.

Outstanding and stable results. Let me regenerate the original baseline into a temp file and produce a clean side-by-side comparison table to document the full improvement.

bash

cd /app && cp /app/outputs/model.pt /tmp/final_keep.pt && python3 train.py > /tmp/baseline.log 2>&1 && cp /app/outputs/model.pt /tmp/model_baseline.pt && cp /tmp/final_keep.pt /app/outputs/model.pt && echo "baseline regenerated; final restored to outputs" && tail -2 /tmp/baseline.log

output

baseline regenerated; final restored to outputs
[train] epoch=10/10 mse=1.0889e-05 val_rel_l2=0.0028
[train] wrote /app/outputs/model.pt

Good , I temporarily saved the final, let the baseline overwrite outputs, copied the baseline to /tmp, then restored the final to outputs. Now let me produce a clean comparison table and verify outputs still holds run4 (final).

bash

cd /app && python3 -c "
import json, torch
from eval_rollout import load_model, rollout_metrics
cfg=json.load(open('task_config.json')); dev='cuda'
models={'baseline (train.py)':'/tmp/model_baseline.pt','FINAL (outputs/model.pt)':'/app/outputs/model.pt'}
data={s:torch.load(f'data/{s}/trajectories.pt',weights_only=True)['trajectories'].float() for s in ['val','public_test']}
print(f'{\"model\":28s} {\"split\":12s} {\"roll_mean\":>10s} {\"agg\":>8s} {\"final\":>8s} {\"rmse\":>8s}')
print('-'*80)
for name,p in models.items():
    m,fm,fs=load_model(p,cfg,dev)
    for s in ['val','public_test']:
        r=rollout_metrics(m,data[s],fm,fs,device=dev,starts=[4])
        print(f'{name:28s} {s:12s} {r[\"rollout_rel_l2_mean\"]:10.4f} {r[\"rollout_rel_l2_agg\"]:8.4f} {r[\"final_step_rel_l2\"]:8.4f} {r[\"rollout_rmse\"]:8.4f}')
    print('-'*80)
# improvement factors
mb,fmb,fsb=load_model(models['baseline (train.py)'],cfg,dev)
mf,fmf,fsf=load_model(models['FINAL (outputs/model.pt)'],cfg,dev)
for s in ['val','public_test']:
    rb=rollout_metrics(mb,data[s],fmb,fsb,device=dev,starts=[4])
    rf=rollout_metrics(mf,data[s],fmf,fsf,device=dev,starts=[4])
    print(f'{s}: rollout_mean {rb[\"rollout_rel_l2_mean\"]/rf[\"rollout_rel_l2_mean\"]:.1f}x better, final-step {rb[\"final_step_rel_l2\"]/rf[\"final_step_rel_l2\"]:.1f}x better')
"

output

model                        split         roll_mean      agg    final     rmse
--------------------------------------------------------------------------------
baseline (train.py)          val              0.0099   0.0146   0.0192   0.0171
baseline (train.py)          public_test      0.0082   0.0148   0.0149   0.0186
--------------------------------------------------------------------------------
FINAL (outputs/model.pt)     val              0.0023   0.0034   0.0030   0.0041
FINAL (outputs/model.pt)     public_test      0.0016   0.0023   0.0020   0.0029
--------------------------------------------------------------------------------
val: rollout_mean 4.4x better, final-step 6.3x better
public_test: rollout_mean 5.2x better, final-step 7.3x better

The final model is 4.4-5.2x better on rollout mean and 6.3-7.3x better on final-step rel L2, on both val and public_test. Let me do a final sanity check that the output file is correct and confirm everything is in order. Let me also verify the final model one more time with weights_only=True loading and that it matches build_model exactly (no missing/unexpected keys). I already did this, but let me do a final definitive check and list the outputs directory. Let me also make sure I haven't left any background processes running.

The final model is **4.4–5.2× better on rollout-mean** and **6.3–7.3× better on final-step** rel-L2 across both val and public_test. Let me do final housekeeping: confirm no stray processes, verify the saved file once more, and list the output.

bash

cd /app && echo "--- background jobs ---" && jobs -l; ps aux | grep -E "train_improved|train.py" | grep -v grep | wc -l | xargs echo "running train procs:"; echo "--- outputs ---" && ls -la /app/outputs/ && echo "--- final strict contract check (weights_only=True) ---" && python3 -c "
import json, torch
from candidate_model import build_model
cfg=json.load(open('task_config.json'))
d=torch.load('/app/outputs/model.pt', weights_only=True)
m=build_model(cfg)
ret=m.load_state_dict(d['state_dict'], strict=True)
print('missing/unexpected keys:', ret.missing_keys, ret.unexpected_keys)
print('keys present:', sorted(d.keys()))
print('types: feat_mean=%s feat_std=%s'%(type(d['feat_mean']).__name__, type(d['feat_std']).__name__))
print('ALL GOOD')
"

output

--- background jobs ---
[1]    221 Done                    cd /app && rm -f /tmp/run1.log && nohup env EPOCHS=50 WARMUP=3 LR=2e-3 CURR="0:1,2:2,5:4,10:6,16:8,24:10,36:12" SAVE=/tmp/model_run1.pt python3 train_improved.py > /tmp/run1.log 2>&1
[2]    290 Done                    cd /app && cp /tmp/model_run1.pt /app/outputs/model.pt && ls -la /app/outputs/ && echo "saved run1 as current best" && rm -f /tmp/run2.log && nohup env EPOCHS=64 WARMUP=3 LR=2e-3 CURR="0:1,2:2,4:4,8:6,13:8,19:10,26:12,40:16" SAVE=/tmp/model_run2.pt python3 train_improved.py > /tmp/run2.log 2>&1
[3]    379 Done                    cd /app && cp /tmp/model_run2.pt /app/outputs/model.pt && echo "adopted run2 as current best" && rm -f /tmp/run3.log && nohup env SEED=12345 EPOCHS=70 WARMUP=3 LR=2e-3 CURR="0:1,2:2,4:4,8:6,13:8,19:10,26:12,36:16,52:20" SAVE=/tmp/model_run3.pt python3 train_improved.py > /tmp/run3.log 2>&1
[4]+   430 Done                    cd /app && rm -f /tmp/run4.log && nohup env EPOCHS=80 WARMUP=3 LR=2e-3 CURR="0:1,2:2,4:4,7:6,11:8,16:10,22:12,30:16" SAVE=/tmp/model_run4.pt python3 train_improved.py > /tmp/run4.log 2>&1
running train procs: 0
--- outputs ---
total 548
drwxr-sr-x 2 h2tagent h2tagent   4096 Jun 20 05:52 .
drwxrws--- 1 h2tagent h2tagent   4096 Jun 20 05:54 ..
-rw-r--r-- 1 h2tagent h2tagent 552226 Jun 20 06:34 model.pt
--- final strict contract check (weights_only=True) ---
missing/unexpected keys: [] []
keys present: ['feat_mean', 'feat_std', 'state_dict']
types: feat_mean=float feat_std=float
ALL GOOD

Everything is in order: - No background training processes running - /app/outputs/model.pt exists (552 KB) - Contract fully satisfied: loads with weights_only=True, state_dict matches build_model exactly (no missing/unexpected keys), feat_mean/feat_std are floats The final model is 4.4-5.2x better on rollout mean and 6.3-7.3x better on final-step rel L2 vs baseline. Let me write a brief summary of the approach in a README for documentation, and then I'm done. Actually, the task doesn't strictly require a README, but it's good to document. Let me create a concise one. Actually, let me keep focus , the deliverable is the checkpoint. But a short note documenting the recipe is helpful and harmless. Let me add a brief README to /app/outputs or /app. Let me write a short summary file documenting what I did. Actually, let me reconsider whether I should verify robustness to a potential edge case: what if the grader seeds the rollout using a window taken from a different part of the trajectory, or uses the final frames? I already tested many start points (4 to 74) and the results are consistent and excellent. What if it rolls from the very end needing frames beyond 100? No, trajectories are length 100 and horizon 20, so it must seed at t<=80. My many-start test covered up to start=74 predicting to 94. Good. One more potential concern: the grader might use the public_test set OR a hidden test set with the same distribution. Since my model generalizes from train→val→public_test consistently (and public_test is actually easier), a hidden test of the same distribution should be fine. Let me also double check: the model outputs are finite and stable, yes, verified, and H40 rollout stays bounded. I'm confident in the deliverable. Let me write a brief summary doc and finish. Let me create /app/outputs/README or /app/SOLUTION_NOTES.md documenting the recipe. I'll put it at /app/SOLUTION_NOTES.md to avoid cluttering outputs (grader reads model.pt from outputs). Actually, I want to be careful not to place anything in outputs that could confuse the grader. I'll put notes at /app/SOLUTION_NOTES.md.

All clean: no stray processes, `model.pt` present (552 KB), and the contract is fully satisfied (loads with `weights_only=True`, state_dict matches `build_model` with no missing/unexpected keys, `feat_mean`/`feat_std` are floats). Let me record a concise summary of the recipe for documentation (kept outside `outputs/` so nothing interferes with the grader reading `model.pt`).

str_replace_based_edit_tool

command: create
path: /app/SOLUTION_NOTES.md
file_text: # KS-1D Forecasting , Training Recipe

Deliverable: `/app/outputs/model.pt` (dict: `state_dict`, `feat_mean`, `feat_std`),
compatible with `build_model(task_config)` and loadable via `torch.load(..., weights_only=True)`.
Architecture and model config (width/modes/layers) are UNCHANGED , only the training recipe changed.
Reproduce with: `python3 /app/train_improved.py` (see env knobs at top of the file).

## Problem framing
The grader rolls the model autoregressively for 20 steps and scores the full rollout
(relative-L2). The baseline minimises only single-step MSE, so its per-step error
compounds (val: 0.003 at step 1 -> 0.019 at step 20). The fix is to train for
rollout stability, not just one-step accuracy.

## What changed vs the baseline
1. **Multi-step rollout (BPTT) training with a curriculum.** Each batch unrolls the
   model R steps autoregressively (feeding its own predictions) and the loss is the
   mean per-step relative-L2 over all R steps , directly optimising the graded quantity.
   R follows a curriculum `1 -> 2 -> 4 -> 6 -> 8 -> 10 -> 12 -> 16`, so early epochs
   learn one-step dynamics and later epochs harden long-horizon stability.
2. **Relative-L2 loss** (computed in physical units) instead of MSE , matches the metric.
3. **Exact KS symmetry augmentation**, applied consistently to each (window+targets)
   sequence on-GPU:
   - periodic translation (random per-sample roll along x),
   - reflection+negation `u(x) -> -u(-x)` (a true symmetry of KS; plain reflection is not).
   This enforces translation invariance and multiplies the effective dataset.
4. **EMA of weights with decay-warmup**, plus warmup + cosine LR. Both raw and EMA
   weights are evaluated every epoch by actual 20-step rollout on val; the best is kept.
5. More epochs (fast: model is ~71k params; ~10s/epoch at R=16 on an H100).
6. Gradient clipping for BPTT stability.

## Final config (run4, adopted)
`EPOCHS=80 LR=2e-3 WARMUP=3 WD=1e-4 BS=128 EMA=0.999 grad_clip=1.0`
`CURR=0:1,2:2,4:4,7:6,11:8,16:10,22:12,30:16` (R reaches 16 by epoch 30, long low-LR tail).

## Results , grader-style 20-step autoregressive rollout (seed = first 4 frames)
| split        | metric            | baseline | final  | improvement |
|--------------|-------------------|----------|--------|-------------|
| val          | rollout rel-L2 mean | 0.0099 | 0.0023 | 4.4x |
| val          | final-step rel-L2   | 0.0192 | 0.0030 | 6.3x |
| public_test  | rollout rel-L2 mean | 0.0082 | 0.0016 | 5.2x |
| public_test  | final-step rel-L2   | 0.0149 | 0.0020 | 7.3x |

Per-step error is now nearly flat (~0.002 -> 0.003 over 20 steps) instead of compounding.
Robustness checks: consistent across many rollout start points (t=4..74), and stable well
beyond the eval horizon (40-step rollout stays ~0.003-0.005), confirming the model learned
stable dynamics rather than overfitting to horizon 20. Chosen among 4 runs by a robust
many-start val+public_test average.

output

New file created successfully at /app/SOLUTION_NOTES.md

I've completed the task. Here's a summary of what I did. ## Summary I trained a neural surrogate for the chaotic 1D Kuramoto–Sivashinsky equation and saved an improved checkpoint to `/app/outputs/model.pt`, satisfying the output contract (a dict with `state_dict`, `feat_mean`, `feat_std`, loadable via `torch.load(..., weights_only=True)` and matching `build_model(task_config)`). The model architecture/config were left untouched , only the training recipe changed, as instructed. ### Diagnosis The grader scores a **20-step autoregressive rollout**, but the baseline only minimizes single-step MSE. I verified its per-step error compounds badly (val: 0.003 at step 1 → 0.019 at step 20). So the real objective is rollout *stability*, not one-step accuracy. ### Key changes (all within the allowed scope: loss, optimizer, scheduler, augmentation, EMA, epochs) 1. **Multi-step rollout/BPTT training with a curriculum** , each batch unrolls the model autoregressively for R steps (feeding its own predictions) and optimizes the mean per-step relative-L2, directly matching what's graded. R ramps `1→2→4→6→8→10→12→16`. 2. **Relative-L2 loss** instead of MSE, matching the metric. 3. **Exact KS symmetry augmentation** (on-GPU, applied consistently to window+targets): periodic translation, and reflection-with-negation `u(x)→−u(−x)` , I worked out that plain reflection is *not* a KS symmetry but reflection+negation is. 4. **EMA with decay-warmup**, warmup+cosine LR, gradient clipping, more epochs; both raw and EMA weights are evaluated each epoch by real 20-step rollout and the best is kept. ### Results (grader-style 20-step rollout) | split | metric | baseline | final | improvement | |---|---|---|---|---| | val | rollout rel-L2 mean | 0.0099 | 0.0023 | **4.4×** | | val | final-step rel-L2 | 0.0192 | 0.0030 | **6.3×** | | public_test | rollout rel-L2 mean | 0.0082 | 0.0016 | **5.2×** | | public_test | final-step rel-L2 | 0.0149 | 0.0020 | **7.3×** | Per-step error is now nearly flat (~0.002→0.003) rather than compounding. I confirmed this is genuine stability , not horizon-20 overfitting , by checking that rollouts stay bounded well past the eval horizon (40 steps ≈ 0.003–0.005) and are consistent across many start points. I ran four configurations and selected the final one via a robust many-start val+public_test average. A couple of things worth flagging for your judgment: - I selected the checkpoint against val/public_test, which share the training distribution; if the hidden grader set differs substantially, the exact numbers could shift (though generalization train→val→public_test was consistent, and public_test was actually easier). - I assumed the standard normalization convention (grader normalizes inputs and denormalizes outputs with `feat_mean`/`feat_std`, as in the provided `train.py`); my local evaluator mirrors this. Supporting files: `train_improved.py` (reproducible trainer), `eval_rollout.py` (rollout harness), and `SOLUTION_NOTES.md` (detailed write-up). No background jobs remain running.

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: ks-equation-1d-forecast
# attempt: 5  status: completed  raw_score: 0.44
# reward rule: continuous: score 0.44 < 0.5
# => reward = 0.0  (FAIL)
#
# 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).
----------------------------------------------------------------------
--- background jobs ---
[1]    221 Done                    cd /app && rm -f /tmp/run1.log && nohup env EPOCHS=50 WARMUP=3 LR=2e-3 CURR="0:1,2:2,5:4,10:6,16:8,24:10,36:12" SAVE=/tmp/model_run1.pt python3 train_improved.py > /tmp/run1.log 2>&1
[2]    290 Done                    cd /app && cp /tmp/model_run1.pt /app/outputs/model.pt && ls -la /app/outputs/ && echo "saved run1 as current best" && rm -f /tmp/run2.log && nohup env EPOCHS=64 WARMUP=3 LR=2e-3 CURR="0:1,2:2,4:4,8:6,13:8,19:10,26:12,40:16" SAVE=/tmp/model_run2.pt python3 train_improved.py > /tmp/run2.log 2>&1
[3]    379 Done                    cd /app && cp /tmp/model_run2.pt /app/outputs/model.pt && echo "adopted run2 as current best" && rm -f /tmp/run3.log && nohup env SEED=12345 EPOCHS=70 WARMUP=3 LR=2e-3 CURR="0:1,2:2,4:4,8:6,13:8,19:10,26:12,36:16,52:20" SAVE=/tmp/model_run3.pt python3 train_improved.py > /tmp/run3.log 2>&1
[4]+   430 Done                    cd /app && rm -f /tmp/run4.log && nohup env EPOCHS=80 WARMUP=3 LR=2e-3 CURR="0:1,2:2,4:4,7:6,11:8,16:10,22:12,30:16" SAVE=/tmp/model_run4.pt python3 train_improved.py > /tmp/run4.log 2>&1
running train procs: 0
--- outputs ---
total 548
drwxr-sr-x 2 h2tagent h2tagent   4096 Jun 20 05:52 .
drwxrws--- 1 h2tagent h2tagent   4096 Jun 20 05:54 ..
-rw-r--r-- 1 h2tagent h2tagent 552226 Jun 20 06:34 model.pt
--- final strict contract check (weights_only=True) ---
missing/unexpected keys: [] []
keys present: ['feat_mean', 'feat_std', 'state_dict']
types: feat_mean=float feat_std=float
ALL GOOD

Reproduce this trial: git checkout 2f94510 && PYTHONPATH=src python3 scripts/build_site.py , then open trial/trial_3d3357504ab54501. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.

Trial trial_3d3357504ab54501 · verifier authoritative; classifier explanatory.