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.

SubtypeIncomplete Optimization - Agent Chose Correct Strategy but Didn't Achieve Sufficient Performance Margin

EvidenceTest output shows raw_score: 0.46 vs. required 0.5 threshold. Agent achieved ~5.2-5.5x improvement over baseline (rollout_rel_l2: 0.0095 → ~0.0019-0.0020) but needed ~5.9x to reach target 0.0016 for passing reward. The summary states: "score 0.46 < 0.5 => reward = 0.0 (FAIL)". Task config shows weak baseline (10ep MSE) gets reward 0.0 at rl2=0.0095; golden solution gets 0.50 at rl2=0.0015. Agent came within 0.04 reward points of passing.

Root causeThe agent identified and implemented the fundamentally correct optimization strategy (multi-step autoregressive rollout training with curriculum, augmentation, EMA, and relative-L2 loss), but fell slightly short of achieving the necessary performance margin. The gap between their 0.46 score and the 0.5 passing threshold represents a ~3-4% shortfall in metric improvement needed, suggesting hyperparameter tuning or additional training epochs could have closed the gap.

RecommendationN/A - task is fine. This is a hard task with a narrow passing margin (0.5 reward requires rl2≤0.0016, a 15x improvement over weak baseline). The agent demonstrated good understanding by implementing multi-step training but needed either: (1) more epochs to fully converge, (2) finer hyperparameter tuning (learning rate, EMA decay, noise schedule), or (3) additional augmentation strategies. The task specification is clear and the evaluation is fair, the agent was simply close but not quite there.

Trajectory

Tool-by-tool agent trajectory

187 tool calls · 3 tool types · 187 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 06:28 ..
-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===" && cat candidate_model.py && echo "===DATASET===" && cat dataset.py && echo "===TRAIN===" && cat train.py

output

===CANDIDATE MODEL===
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===
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===
from __future__ import annotations
import json

…[truncated]

Now let me understand the data and the evaluation. Let me check the hardware, data shapes, and understand the structure.

Now let me inspect the hardware available and the data characteristics.

bash

cd /app && python3 -c "
import torch, json
print('torch', torch.__version__)
print('cuda available:', torch.cuda.is_available())
if torch.cuda.is_available():
    print('device:', torch.cuda.get_device_name(0))
    print('mem GB:', torch.cuda.get_device_properties(0).total_memory/1e9)
import os
print('CPU count:', os.cpu_count())
"
echo "---NVIDIA-SMI---"
nvidia-smi 2>/dev/null || echo "no nvidia-smi"
echo "---MEM---"
free -h 2>/dev/null | head -3

output

torch 2.5.1+cu124
cuda available: True
device: NVIDIA H100 80GB HBM3
mem GB: 85.017624576
CPU count: 3
---NVIDIA-SMI---
Sat Jun 20 06:29:10 2026       
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 580.82.07              Driver Version: 580.82.07      CUDA Version: 13.0     |
+-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  NVIDIA H100 80GB HBM3          Off |   N/A              Off |                    0 |
| N/A   39C    P0            292W /  700W |    3121MiB /  81559MiB |      0%      Default |
|                                         |                        |             Disabled |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|  No running processes found                                                             |
+-----------------------------------------------------------------------------------------+
---MEM---
               total        used        free      shared  buff/cache   available
Mem:            30Gi        64Mi        29Gi          0B        29Mi        29Gi
Swap:             0B          0B          0B

I have an H100 GPU available. Let me inspect the actual data to understand its structure and statistics.

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)
    print('===', split, '===')
    print('keys:', list(p.keys()))
    t = p['trajectories']
    print('shape:', t.shape, 'dtype:', t.dtype)
    print('mean: %.5f std: %.5f min: %.4f max: %.4f' % (t.mean(), t.std(), t.min(), t.max()))
"

output

=== train ===
keys: ['trajectories']
shape: torch.Size([200, 100, 128]) dtype: torch.float32
mean: -0.00000 std: 1.15997 min: -3.2449 max: 3.2476
=== val ===
keys: ['trajectories']
shape: torch.Size([32, 100, 128]) dtype: torch.float32
mean: -0.00000 std: 1.13391 min: -3.1972 max: 3.2103
=== public_test ===
keys: ['trajectories']
shape: torch.Size([16, 100, 128]) dtype: torch.float32
mean: 0.00000 std: 1.22519 min: -3.1724 max: 3.1835

Let me understand the data better. The data is already zero-mean. Let me check some properties of the KS trajectories, spatial smoothness, energy spectrum, and temporal correlation. Let me also understand how the evaluation works: the grader rolls the model autoregressively for 20 steps. Let me think about the evaluation setup. Given a window of 4 frames, predict the next frame. Then the grader does autoregressive rollout for 20 steps. So starting from frames [t-4, t-3, t-2, t-1], predict frame t. Then use [t-3, t-2, t-1, t_pred] to predict t+1, etc., for 20 steps. The scoring is `rollout_rel_l2_score`. Let me think about what that means. Likely relative L2 over the whole rollout: for each predicted frame, compute ||pred - true|| / ||true||, average over 20 steps. The "score" might be something like max(0, 1 - rel_l2) or similar. Let me focus on minimizing rollout rel L2. Key insight: The baseline trains on single-step prediction with MSE. But the evaluation is autoregressive rollout over 20 steps. The classic problem is that single-step training doesn't account for error accumulation (distribution shift during rollout). The standard fix is **pushforward / multi-step training** (training on rollouts), adding noise to inputs to make the model robust to its own errors, etc. Let me think about the best approach: 1. **Multi-step rollout training (pushforward trick)**: Train the model to do several autoregressive steps and backprop through them (or use the pushforward trick where you don't backprop through all steps). This directly optimizes what's evaluated. 2. **Noise injection**: Add noise to input frames to simulate the error distribution during rollout. This is the "training noise" approach from Stachenfeld et al., and works well for stabilizing rollouts. 3. **Better loss**: rel L2 loss instead of MSE, maybe combined with spectral loss. Since the metric is relative L2, training with a relative L2 loss might help. 4. **EMA of weights**: stabilizes and often improves. 5. **Longer training, cosine schedule with warmup.** Let me think carefully. The dataset has 200 trajectories × 100 timesteps. With k=4, that's 96 windows per trajectory = 19200 training pairs. That's a decent amount. The model is fixed (FNO1d with width=32, modes=16, 4 layers). in_channels=4, out_channels=1. I can't change architecture but I can change training. Let me think about what will give the biggest win. For chaotic PDE forecasting with autoregressive rollout, the biggest wins typically come from: - Training on multi-step rollouts (so the model sees its own predictions) - Noise injection - The pushforward trick Let me plan to implement a training loop that: 1. Standardizes data. 2. Trains with a curriculum: start with single-step, then increase rollout length (pushforward). 3. Uses a relative L2 loss (matching eval metric) possibly plus a bit of MSE or gradient/spectral loss. 4. Uses AdamW with cosine schedule + warmup. 5. Uses EMA. 6. Adds small noise to inputs. Let me think about the rollout mechanics carefully, because I need to replicate the grader's rollout during training to optimize it. The dataset yields (window [4,128], next_frame [1,128]). For rollout, I need consecutive frames. Given a trajectory of length 100, I can sample a starting index t (>=4), take window = frames[t-4:t], then the targets are frames[t:t+H] for H steps. At each step, predict next frame, append to window (drop oldest), repeat. For the grader: "rolls your model autoregressively for 20 steps and scores the full rollout." So likely: given initial window (the last 4 frames of some context), predict 20 future frames, compare to ground truth 20 frames. Wait, but each val/test trajectory has 100 frames. The grader probably takes the first 4 frames as the initial window and rolls out 20 steps predicting frames 4..23, comparing to ground truth. Or maybe it uses multiple starting points. I don't know exactly, but optimizing multi-step rollout from arbitrary starting po …[truncated]

The data is already zero-mean (global standardization ~ mean 0, std ~1.16). Let me understand the KS dynamics a bit and check temporal/spatial structure, then run the baseline for reference.

bash

cd /app && python3 -c "
import torch
t = torch.load('data/train/trajectories.pt', weights_only=True)['trajectories']
# temporal autocorrelation between consecutive frames
d = t[:,1:]-t[:,:-1]
print('consecutive frame diff rms:', d.pow(2).mean().sqrt().item())
print('frame rms:', t.pow(2).mean().sqrt().item())
# rel L2 of persistence (predict next=current) over one step
num = (t[:,1:]-t[:,:-1]).pow(2).sum(-1).sqrt()
den = t[:,1:].pow(2).sum(-1).sqrt()
print('persistence 1-step rel L2:', (num/den).mean().item())
# persistence over 20-step rollout (predict frame t+h = frame t)
base = t[:,0:1]
rl2=[]
for h in range(1,21):
    num=(t[:,h]-t[:,0]).pow(2).sum(-1).sqrt(); den=t[:,h].pow(2).sum(-1).sqrt()
    rl2.append((num/den).mean().item())
print('persistence rollout rel L2 mean over 20:', sum(rl2)/len(rl2))
print('per-step h=1,5,10,20:', rl2[0], rl2[4], rl2[9], rl2[19])
"

output

consecutive frame diff rms: 0.05559837818145752
frame rms: 1.159973382949829
persistence 1-step rel L2: 0.03789564222097397
persistence rollout rel L2 mean over 20: 0.33784335739910604
per-step h=1,5,10,20: 0.03777714818716049 0.17969542741775513 0.3369041085243225 0.590832531452179

Interesting. So consecutive frames are quite close (dt_effective=0.25, small changes). Persistence (predict next = current) gives 1-step rel L2 of 0.038. Over a 20-step rollout, persistence gives mean rel L2 of 0.34 and final step 0.59. So the model needs to beat persistence substantially. The changes per step are small (rms 0.056 vs frame rms 1.16), so the model mostly needs to predict small increments. This suggests that predicting the residual (delta) might be easier, but the architecture is fixed and outputs the next frame directly. That's fine; the model can learn to output something close to the input. Actually, this is a key insight: since frames change slowly, the model could benefit from a "skip"/residual formulation where it predicts next_frame = last_frame + delta. But the architecture is fixed (outputs next frame directly). However, I control the training, and I could train the model to predict the residual and then reconstruct... but no , the grader calls build_model and uses the model's forward directly, feeding normalized window and expecting normalized next frame (presumably). Wait, let me check how the grader uses feat_mean/feat_std. The output contract: model.pt contains state_dict, feat_mean, feat_std. The grader presumably: 1. Builds model, loads state_dict. 2. Normalizes input window with (x - feat_mean)/feat_std. 3. Runs model to get normalized prediction. 4. Denormalizes: pred * feat_std + feat_mean. 5. Appends to window, rolls out. So the model's forward must map normalized window → normalized next frame. I cannot change the normalization convention (the grader does it). But I CAN choose feat_mean and feat_std values. And I must train the model consistent with whatever the grader does. Wait , but I don't know exactly what the grader does with feat_mean/feat_std. Let me think. The contract says feat_mean, feat_std are "float scalars for input/output standardization." The baseline's normalize function: (t - feat_mean)/feat_std, and for val it denormalizes pred*feat_std+feat_mean. So the grader almost certainly does the same: normalize input window, run model, denormalize output. So my model must be trained to take normalized input and produce normalized output. That's exactly what the baseline does. Good. So I should keep the same convention: standardize with global mean/std, model works in normalized space. Now, the residual idea: I can't make the architecture output residual explicitly, but the model can learn the near-identity mapping. FNO with the bypass conv can represent identity-ish maps reasonably. Actually, predicting a near-identity is somewhat hard for these models to nail to high precision, which is why residual/skip connections help. But I can't modify the architecture's forward... Hmm, wait. Actually let me reconsider. Could I bake a residual connection into the weights? No. The forward is fixed: lift → blocks → proj. There's no global skip from input to output. So the model must produce the full next frame from scratch in feature space. This is fine but means the model has to learn to reproduce the input plus a small change. One trick: I could train it to predict the residual delta = next - last_frame, in normalized space, but then the grader would denormalize assuming it's the full frame. That wouldn't work because the grader does pred*std+mean and treats it as the frame. So no. Unless... I think about it differently. The grader feeds normalized window, gets model output, denormalizes, uses as next frame. If I want the model to effectively predict residual, I'd need the model's forward to output (last_frame_normalized + delta_normalized). Since the model can't see do a global skip, it would have to reconstruct last_frame in its output. That's what it learns anyway. So there's no free lunch from the residual trick given the fixed architecture. The model just has to learn it. OK so the main levers are: 1. Multi-step rollout training (pushforward) , biggest win for rollout metric. …[truncated]

Key observations: - H100 GPU available; data is tiny (200×100×128 = ~10MB) so I can keep everything GPU-resident and avoid dataloader overhead. - Frames change slowly per step (diff rms 0.056 vs frame rms 1.16). Persistence baseline gives 1-step rel L2 ≈ 0.038 but degrades to 0.34 mean / 0.59 final over a 20-step rollout , so **error accumulation over the autoregressive rollout is the real challenge**, which single-step MSE training (the baseline) does not address. Let me run the baseline briefly to get reference numbers and timing.

bash

cd /app && mkdir -p outputs && TASK_EPOCHS_OVERRIDE=10 timeout 600 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

The baseline achieves 1-step val rel L2 of 0.0028 after 10 epochs. That's single-step. But the real metric is 20-step rollout. Let me write a rollout evaluator to see how this baseline does on the actual task. Let me build that evaluator and measure the baseline checkpoint.

The baseline gets 1-step val rel L2 ≈ 0.0028. But the grader measures a 20-step autoregressive rollout. Let me build a proper rollout evaluator that mimics the grader and measure the baseline checkpoint on the rollout metric.

bash

cd /app && cat > /app/rollout_eval.py << 'EOF'
from __future__ import annotations
import json, os, sys
from pathlib import Path
import torch
sys.path.insert(0, "/app")
from candidate_model import build_model

@torch.no_grad()
def rollout_metrics(model, traj, feat_mean, feat_std, k=4, H=20, device="cuda", starts=None):
    """traj: [N, T, X] ground truth. Roll out H steps from given starts; rel L2 per step."""
    model.eval()
    N, T, X = traj.shape
    if starts is None:
        starts = [k]  # predict frames k..k+H-1 from window [0:k]
    all_step_rl2 = []
    for s in starts:
        if s + H > T:  # not enough future frames
            continue
        window = traj[:, s-k:s].to(device)  # [N,k,X]
        win = (window - feat_mean) / feat_std
        preds = []
        for h in range(H):
            out = model(win)  # [N,1,X] normalized
            preds.append(out[:, 0])
            win = torch.cat([win[:, 1:], out], dim=1)
        pred = torch.stack(preds, dim=1) * feat_std + feat_mean  # [N,H,X]
        true = traj[:, s:s+H].to(device)
        num = (pred - true).pow(2).sum(-1).sqrt()
        den = true.pow(2).sum(-1).sqrt().clamp_min(1e-8)
        all_step_rl2.append((num / den))  # [N,H]
    rl2 = torch.cat(all_step_rl2, dim=0)  # [sum_starts*N, H]
    per_step = rl2.mean(0)  # [H]
    return {
        "rollout_rel_l2": rl2.mean().item(),
        "final_step_rel_l2": per_step[-1].item(),
        "per_step": per_step.cpu().tolist(),
    }

if __name__ == "__main__":
    with open("/app/task_config.json") as f: cfg = json.load(f)
    device = "cuda"
    ckpt = torch.load(sys.argv[1] if len(sys.argv)>1 else "/app/outputs/model.pt", weights_only=True)
    model = build_model(cfg).to(device)
    model.load_state_dict(ckpt["state_dict"])
    fm, fs = ckpt["feat_mean"], ckpt["feat_std"]
    for split in ["val","public_test"]:
        traj = torch.load(f"/app/data/{split}/trajectories.pt", weights_only=True)["trajectories"].float()
        # primary: start at k. Also multi-start average.
        m_first = rollout_metrics(model, traj, fm, fs, starts=[4], device=device)
        multi = list(range(4, traj.shape[1]-20, 8))
        m_multi = rollout_metrics(model, traj, fm, fs, starts=multi, device=device)
        print(f"[{split}] start@4: rollout_rel_l2={m_first['rollout_rel_l2']:.4f} final={m_first['final_step_rel_l2']:.4f}")
        print(f"[{split}] multi-start({len(multi)}): rollout_rel_l2={m_multi['rollout_rel_l2']:.4f} final={m_multi['final_step_rel_l2']:.4f}")
        print(f"[{split}] per-step(first): " + " ".join(f"{v:.3f}" for v in m_first['per_step']))
EOF
python3 /app/rollout_eval.py /app/outputs/model.pt

output

[val] start@4: rollout_rel_l2=0.0099 final=0.0192
[val] multi-start(10): rollout_rel_l2=0.0093 final=0.0176
[val] per-step(first): 0.003 0.003 0.004 0.005 0.006 0.006 0.007 0.008 0.008 0.009 0.010 0.010 0.011 0.012 0.013 0.014 0.016 0.017 0.018 0.019
[public_test] start@4: rollout_rel_l2=0.0082 final=0.0149
[public_test] multi-start(10): rollout_rel_l2=0.0077 final=0.0144
[public_test] per-step(first): 0.002 0.003 0.003 0.004 0.005 0.005 0.006 0.007 0.007 0.008 0.008 0.009 0.010 0.010 0.011 0.012 0.012 0.013 0.014 0.015

So the baseline (10 epochs, single-step MSE) already achieves rollout rel L2 of ~0.0099 on val (start@4), final step 0.0192. That's much better than persistence (0.34). The error grows roughly linearly over the rollout from 0.003 to 0.019. Now my job is to push this lower. The error accumulation pattern (growing over rollout) is exactly what multi-step training addresses. Let me implement the improved training. Key levers: 1. Multi-step rollout training (curriculum on H) , directly target rollout. 2. Noise injection on inputs. 3. Symmetry augmentation (periodic shift + reflection/negation). 4. rel L2 loss (matches metric). 5. EMA. 6. More epochs, warmup+cosine schedule. Let me write a comprehensive training script. I'll keep data on GPU. Let me design carefully. Let me think about the batch construction for multi-step training: - valid starts for horizon H: t in [k, T-H] for each trajectory → gather window [B,k,X] and targets [B,H,X]. - Actually to have a target for each of H steps, need frames t..t+H-1, and window t-k..t-1. So t ranges in [k, T-H]. With T=100, H up to say 12, plenty of samples. Let me build an index of (i, t) pairs. For training efficiency with curriculum, I'll just regenerate valid (i,t) per epoch based on current H. Augmentation (applied to the combined [B, k+H, X] block before splitting): - periodic shift per-sample - reflection+negation for random half Let me implement per-sample periodic shift with gather. Let me write the loss: relative L2 averaged over steps and batch. per step: ||pred-true|| / ||true|| computed on normalized? or physical? The metric uses physical (denormalized) values, but since it's relative L2 and normalization is just affine (x-mean)/std with scalar mean/std, the relative L2 in normalized vs physical differ because subtracting mean changes norms. Actually the metric denormalizes then computes rel L2 on physical u. Since feat_mean≈0 (data is zero mean), normalized = u/std approx, so rel L2 is nearly identical in both spaces. But to be exact, I'll compute the loss in physical space (denormalize pred and true) to match the metric. Actually simpler: since feat_mean is ~0, and I compute loss as rel L2, I can compute in physical units by denormalizing. Let me just compute loss in physical units to exactly match. Hmm, but working in physical units, the model outputs normalized; I denormalize (×std + mean). rel L2 = ||(pred_n - true_n)*std|| / ||true_n*std + mean||... the mean adds a constant vector (mean * ones). Since mean≈0 it's negligible. I'll denormalize properly to match metric exactly. Actually, let me reconsider whether to train loss in normalized or physical space. For gradient scaling it doesn't matter much. I'll compute rel L2 in physical space (denormalized) to exactly match the eval metric. Both pred and true denormalized. Now, during rollout training, the input window must be normalized (that's what model expects). So I keep a normalized window, predict normalized next, append. For loss, denormalize. Noise injection: add Gaussian noise to the normalized window before feeding. Std of noise maybe ~ a fraction. Let me think in terms of the typical one-step error. The model's one-step error is rel L2 ~0.003, i.e., error magnitude ~0.003 * ||frame||. In normalized space frame norm ~ sqrt(128)*1 ≈ 11.3, so error ~0.034 in L2, per-component ~0.003. Hmm that's tiny. But over rollout, errors grow. The noise should mimic the error distribution the model sees during rollout. With multi-step training, the model generates its own errors, so explicit noise may be less critical, but a small amount helps. Let me add noise with std ~0.01-0.05 in normalized units to the input window (the pushforward idea). Actually, with multi-step unrolling the model already sees its own errors. I'll add a modest noise too. Let me make noise configurable and tune. Let me reconsider the overall approach to maximize the chance of big improvement and stability: Plan A: Curriculum unrolled training …[truncated]

The baseline already does reasonably (rollout rel L2 ≈ 0.0099 on val), but error grows monotonically across the rollout (0.003 → 0.019) , the signature of distribution shift that single-step training ignores. My plan to improve the rollout metric: 1. **Multi-step rollout training** (the key lever) , unroll the model autoregressively during training so it learns to correct its own accumulated errors, with a curriculum increasing horizon. 2. **Exact KS symmetry augmentation** , periodic spatial shifts and the reflect+negate parity symmetry `u(x)→−u(−x)`. 3. **Relative-L2 loss** matching the eval metric, **noise injection**, **EMA**, warmup+cosine schedule, more epochs. Let me write a flexible training script and iterate on configs.

bash

cd /app && cat > /app/train_improved.py << 'EOF'
from __future__ import annotations
import json, os, sys, copy, math, time
from pathlib import Path
import torch
import torch.nn.functional as F
sys.path.insert(0, "/app")
from candidate_model import build_model
from rollout_eval import rollout_metrics

APP = Path("/app")

def get(name, default, cast):
    return cast(os.environ.get(name, default))

def main():
    with (APP/"task_config.json").open() as f: cfg = json.load(f)
    device = "cuda"
    torch.manual_seed(int(cfg["seed"]))
    k = int(cfg["in_channels"])

    # ---- config (env-overridable) ----
    EPOCHS      = get("EP", 80, int)
    BS          = get("BS", 128, int)
    LR          = get("LR", 2e-3, float)
    WD          = get("WD", 1e-4, float)
    WARMUP      = get("WARMUP", 5, int)
    HMAX        = get("HMAX", 10, int)          # max rollout horizon in training
    MODE        = get("MODE", "bptt", str)      # bptt | pushforward
    BPTT        = get("BPTT", 4, int)           # truncated bptt window (detach older)
    NOISE       = get("NOISE", 0.01, float)     # input noise std (normalized units)
    AUG         = get("AUG", 1, int)
    EMA_DECAY   = get("EMA", 0.999, float)
    CLIP        = get("CLIP", 1.0, float)
    SPEC_W      = get("SPEC_W", 0.0, float)     # spectral/gradient loss weight
    OUT         = os.environ.get("OUT", str(APP/"outputs"/"model.pt"))
    SEED        = get("SEED", int(cfg["seed"]), int)
    torch.manual_seed(SEED)

    traj = torch.load(APP/"data"/"train"/"trajectories.pt", weights_only=True)["trajectories"].float().to(device)
    valtraj = torch.load(APP/"data"/"val"/"trajectories.pt", weights_only=True)["trajectories"].float()
    N, T, X = traj.shape
    feat_mean = float(traj.mean().item())
    feat_std  = float(traj.std().clamp_min(1e-6).item())
    fm = torch.tensor(feat_mean, device=device); fs = torch.tensor(feat_std, device=device)
    trajn = (traj - fm) / fs  # normalized, GPU resident

    model = build_model(cfg).to(device)
    opt = torch.optim.AdamW(model.parameters(), lr=LR, weight_decay=WD)
    total_steps_per_epoch = None

    # EMA
    ema = copy.deepcopy(model)
    for p in ema.parameters(): p.requires_grad_(False)
    def ema_update():
        with torch.no_grad():
            for pe, pm in zip(ema.parameters(), model.parameters()):
                pe.mul_(EMA_DECAY).add_(pm, alpha=1-EMA_DECAY)
            for be, bm in zip(ema.buffers(), model.buffers()):
                be.copy_(bm)

    arange_x = torch.arange(X, device=device)
    def augment(block):
        # block: [B, L, X] normalized. periodic shift + reflect/negate (exact KS symmetries)
        B = block.shape[0]
        if AUG:
            shift = torch.randint(0, X, (B,), device=device)
            idx = (arange_x.view(1, X) - shift.view(B, 1)) % X  # [B,X]
            idx = idx.unsqueeze(1).expand(-1, block.shape[1], -1)
            block = torch.gather(block, 2, idx)
            flip = torch.rand(B, device=device) < 0.5
            blk_f = -torch.flip(block, dims=[-1])
            block = torch.where(flip.view(B,1,1), blk_f, block)
        return block

    def curr_H(ep):
        # ramp horizon 1 -> HMAX over training (after warmup)
        if ep < WARMUP: return 1
        frac = (ep - WARMUP) / max(1, (EPOCHS - WARMUP))
        return int(1 + round(frac * (HMAX - 1)))

    def sched_lr(step, steps_per_epoch):
        ep = step / steps_per_epoch
        if ep < WARMUP:
            return LR * (ep / max(1e-8, WARMUP))
        frac = (ep - WARMUP) / max(1e-8, (EPOCHS - WARMUP))
        return 0.5 * LR * (1 + math.cos(math.pi * min(1.0, frac)))

    best = {"val": 1e9}
    gstep = 0
    t0 = time.time()
    for ep in range(EPOCHS):
        H = curr_H(ep)
        # valid starts t in [k, T-H]; build (i,t) index
        ts = torch.arange(k, T - H + 1, device=device)
        ii = torch.arange(N, device=device)
        grid_i, grid_t = torch.meshgrid(ii, ts, indexing="ij")
        pairs = torch.stack([g
…[truncated]

output

written

Let me do a quick short run to test for correctness and timing (few epochs), then scale up.

Let me do a quick smoke test (few epochs) to verify correctness and timing before a full run.

bash

cd /app && EP=6 WARMUP=2 HMAX=4 OUT=/app/outputs/smoke.pt timeout 300 python3 train_improved.py 2>&1 | tail -20

output

[ep 1/6] H=1 lr=9.9e-04 loss=4.5744e-01 val_roll(multi)=1.0132 final=1.0136 start4=1.0126 t=3s *
[ep 2/6] H=1 lr=2.0e-03 loss=3.2901e-02 val_roll(multi)=1.0100 final=1.0116 start4=1.0095 t=5s *
[ep 5/6] H=3 lr=2.6e-04 loss=9.6435e-03 val_roll(multi)=0.9926 final=1.0070 start4=0.9924 t=11s *
[ep 6/6] H=3 lr=0.0e+00 loss=5.3259e-03 val_roll(multi)=0.9832 final=1.0058 start4=0.9830 t=13s *
[done] best val_roll=0.9832 wrote /app/outputs/smoke.pt

There's a bug. The val_roll is ~1.0 which is terrible (means predictions are near zero or uncorrelated). But the training loss is decreasing to 5e-3. The training loss is rel L2 ~ 0.005 which is good, but the eval gives ~1.0. The discrepancy suggests the EMA model being evaluated is bad, OR there's a mismatch. Wait , EMA decay 0.999, and we only did 6 epochs. With ~150 steps/epoch * 6 = 900 steps. EMA with decay 0.999 after 900 steps: the EMA weight on the initial random model is 0.999^900 ≈ 0.40. So EMA is still 40% the random init! That's why EMA eval is bad early. The EMA hasn't caught up. So the problem is just that EMA warmup is slow and 6 epochs isn't enough for EMA with 0.999 to converge. In the real run with 80 epochs (~12000 steps), 0.999^12000 ≈ e^-12 ≈ tiny, so EMA will be fine. But early eval will look bad. But to be safe and to get meaningful eval, let me use EMA decay that adapts (warmup), e.g., decay = min(EMA_DECAY, (1+step)/(10+step)). This is the standard EMA warmup. Let me add that. Also I should evaluate the raw model too, to compare. Also the training loss at ep6 is 5.3e-3 which is rel L2 averaged over H=3 steps. The non-EMA model is likely much better than EMA right now. Let me fix EMA warmup and also print raw model eval. Let me also reconsider: the loss after ep2 (H=1) is 3.3e-2 rel L2 = 0.033 for one step. The baseline got 0.0028 one-step after 10 epochs. After 2 epochs here it's 0.033 , that's early. Fine. Let me add EMA warmup and evaluate both raw and EMA. Let me fix.

The training loss drops nicely but EMA-based val is ~1.0 , that's because with decay 0.999 and only ~900 steps, the EMA still retains ~40% of the random init. In the full 80-epoch run (~12k steps) this vanishes, but I should add standard **EMA warmup** so eval is meaningful throughout and early checkpoints aren't corrupted. Let me also report the raw model for comparison.

str_replace_based_edit_tool

command: str_replace
path: /app/train_improved.py
old_str:     def ema_update():
        with torch.no_grad():
            for pe, pm in zip(ema.parameters(), model.parameters()):
                pe.mul_(EMA_DECAY).add_(pm, alpha=1-EMA_DECAY)
            for be, bm in zip(ema.buffers(), model.buffers()):
                be.copy_(bm)
new_str:     def ema_update(step):
        d = min(EMA_DECAY, (1.0 + step) / (10.0 + step))  # warmup
        with torch.no_grad():
            for pe, pm in zip(ema.parameters(), model.parameters()):
                pe.mul_(d).add_(pm, alpha=1-d)
            for be, bm in zip(ema.buffers(), model.buffers()):
                be.copy_(bm)

output

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

bash

cd /app && sed -i 's/            ema_update()/            ema_update(gstep)/' train_improved.py
# add raw eval alongside ema eval
python3 - << 'EOF'
import re
s = open('/app/train_improved.py').read()
old = '''            m = rollout_metrics(ema, valtraj, feat_mean, feat_std, starts=multi, device=device)
            m1 = rollout_metrics(ema, valtraj, feat_mean, feat_std, starts=[4], device=device)'''
new = '''            m = rollout_metrics(ema, valtraj, feat_mean, feat_std, starts=multi, device=device)
            m1 = rollout_metrics(ema, valtraj, feat_mean, feat_std, starts=[4], device=device)
            mr = rollout_metrics(model, valtraj, feat_mean, feat_std, starts=multi, device=device)'''
s = s.replace(old,new)
s = s.replace('start4={m1[\'rollout_rel_l2\']:.4f} t=', 'start4={m1[\'rollout_rel_l2\']:.4f} raw={mr[\'rollout_rel_l2\']:.4f} t=')
# track best over both ema and raw
old2 = '''            if m["rollout_rel_l2"] < best["val"]:
                best = {"val": m["rollout_rel_l2"], "sd": copy.deepcopy(ema.state_dict())}
                tag = " *"'''
new2 = '''            cand_val, cand_sd = (m["rollout_rel_l2"], ema.state_dict()) if m["rollout_rel_l2"] <= mr["rollout_rel_l2"] else (mr["rollout_rel_l2"], model.state_dict())
            if cand_val < best["val"]:
                best = {"val": cand_val, "sd": copy.deepcopy(cand_sd)}
                tag = " *"'''
s = s.replace(old2,new2)
open('/app/train_improved.py','w').write(s)
print("patched")
EOF
EP=6 WARMUP=2 HMAX=4 OUT=/app/outputs/smoke.pt timeout 300 python3 train_improved.py 2>&1 | tail -10

output

patched
[ep 1/6] H=1 lr=9.9e-04 loss=4.5744e-01 val_roll(multi)=0.0751 final=0.1408 start4=0.0839 raw=0.1347 t=3s *
[ep 2/6] H=1 lr=2.0e-03 loss=3.2901e-02 val_roll(multi)=0.0452 final=0.0860 start4=0.0480 raw=0.1895 t=5s *
[ep 5/6] H=3 lr=2.6e-04 loss=9.6435e-03 val_roll(multi)=0.0145 final=0.0260 start4=0.0177 raw=0.0185 t=12s *
[ep 6/6] H=3 lr=0.0e+00 loss=5.3259e-03 val_roll(multi)=0.0125 final=0.0225 start4=0.0155 raw=0.0120 t=15s *
[done] best val_roll=0.0120 wrote /app/outputs/smoke.pt

Now EMA works with warmup. After just 6 epochs with HMAX=4, we get val_roll 0.0120 (raw) / 0.0125 (ema). The baseline with 10 epochs single-step got 0.0093 multi-start. So this short run isn't better yet, but it's only 6 epochs with small HMAX. The EMA and raw are now close. Good, the mechanics work. Timing: ~15s for 6 epochs with HMAX ramping to 4 → about 2.5s/epoch at low H, more at higher H. A full 80-epoch run with HMAX=10 will have higher H in later epochs (each step = H model calls). Let me estimate: at H=10, each epoch does ~pairs/BS batches. pairs at H=10: N*(T-H+1-k) = 200*(100-10+1-4)=200*87=17400 pairs, /128 = 136 batches, each doing 10 sequential model calls on batch 128. Should still be a few seconds per epoch on H100. Let me just run the full thing and time it. Let me now run a proper full training run with good defaults and see where we land. Let me use: - EP=80, WARMUP=5, HMAX=10, MODE=bptt, BPTT=4, NOISE=0.01, AUG=1, EMA=0.999, LR=2e-3. Let me run it in the background and monitor. Actually let me time a slightly shorter run first to gauge per-epoch cost at full H, then decide. Let me just launch the full run with logging to a file. Actually, let me be strategic. I have limited token budget, so I want to run experiments efficiently. Let me launch the main run in the background and capture logs, while I think. But I also want to compare configs: bptt vs pushforward, with/without aug, noise levels, HMAX. Running many 80-epoch runs is expensive in wall-clock but the GPU is fast. Let me first do a moderate run (EP=60) to validate the approach strongly beats baseline, then tune. Let me launch a full run now.

EMA warmup fixed , EMA and raw now track closely and rollout error drops fast. The mechanics are correct. Now let me launch a full run in the background with strong defaults and monitor it.

bash

cd /app && nohup env EP=80 WARMUP=5 HMAX=10 MODE=bptt BPTT=4 NOISE=0.01 AUG=1 EMA=0.999 LR=2e-3 OUT=/app/outputs/runA.pt python3 train_improved.py > /app/logs_A.txt 2>&1 &
echo "launched PID $!"; sleep 60; echo "---60s---"; cat /app/logs_A.txt

output

launched PID 185
---60s---
[ep 1/80] H=1 lr=4.0e-04 loss=6.3677e-01 val_roll(multi)=0.2385 final=0.4255 start4=0.2817 raw=0.1708 t=4s *
[ep 2/80] H=1 lr=8.0e-04 loss=2.0477e-02 val_roll(multi)=0.0278 final=0.0503 start4=0.0327 raw=0.0507 t=6s *
[ep 5/80] H=1 lr=2.0e-03 loss=3.2414e-02 val_roll(multi)=0.0269 final=0.0496 start4=0.0331 raw=0.1814 t=11s *
[ep 10/80] H=1 lr=2.0e-03 loss=1.6256e-02 val_roll(multi)=0.0152 final=0.0251 start4=0.0182 raw=0.0449 t=19s *
[ep 15/80] H=2 lr=1.9e-03 loss=2.1980e-02 val_roll(multi)=0.0162 final=0.0300 start4=0.0177 raw=0.0805 t=31s
[ep 20/80] H=3 lr=1.8e-03 loss=1.2861e-02 val_roll(multi)=0.0102 final=0.0165 start4=0.0114 raw=0.0286 t=42s *

It's progressing. At ep20 (H=3), val_roll multi = 0.0102 EMA, raw 0.0286. Interesting: at high LR (2e-3) the raw model is noisy/worse but EMA is smoothing it well. The val_roll is improving. Let me let it continue and check later. Note: the raw model is quite a bit worse than EMA during high-LR phase (0.0286 vs 0.0102). That's expected , EMA smooths. As LR decays, raw will improve and converge toward EMA. Let me wait for it to progress further. Meanwhile, let me think about whether LR=2e-3 is too high , the raw model at ep5 had raw=0.1814 (very bad) while EMA=0.0269. That big gap suggests the high LR is causing the raw model to bounce around a lot. EMA saves us. But maybe a lower peak LR would be more stable and faster-converging. Let me note to try LR=1e-3 too. Let me continue monitoring.

Progress looks healthy , EMA rollout error is dropping (0.0102 at ep20, already matching the baseline) while the raw model is noisier at this high LR, with EMA smoothing it nicely. Let me let it continue and check back.

bash

cd /app && sleep 120; cat /app/logs_A.txt | tail -12

output

[ep 2/80] H=1 lr=8.0e-04 loss=2.0477e-02 val_roll(multi)=0.0278 final=0.0503 start4=0.0327 raw=0.0507 t=6s *
[ep 5/80] H=1 lr=2.0e-03 loss=3.2414e-02 val_roll(multi)=0.0269 final=0.0496 start4=0.0331 raw=0.1814 t=11s *
[ep 10/80] H=1 lr=2.0e-03 loss=1.6256e-02 val_roll(multi)=0.0152 final=0.0251 start4=0.0182 raw=0.0449 t=19s *
[ep 15/80] H=2 lr=1.9e-03 loss=2.1980e-02 val_roll(multi)=0.0162 final=0.0300 start4=0.0177 raw=0.0805 t=31s
[ep 20/80] H=3 lr=1.8e-03 loss=1.2861e-02 val_roll(multi)=0.0102 final=0.0165 start4=0.0114 raw=0.0286 t=42s *
[ep 25/80] H=3 lr=1.7e-03 loss=1.0746e-02 val_roll(multi)=0.0094 final=0.0153 start4=0.0102 raw=0.0322 t=54s *
[ep 30/80] H=4 lr=1.5e-03 loss=1.6183e-02 val_roll(multi)=0.0079 final=0.0129 start4=0.0083 raw=0.0809 t=69s *
[ep 35/80] H=4 lr=1.3e-03 loss=8.1735e-03 val_roll(multi)=0.0076 final=0.0119 start4=0.0079 raw=0.0207 t=84s *
[ep 40/80] H=5 lr=1.1e-03 loss=7.6924e-03 val_roll(multi)=0.0061 final=0.0095 start4=0.0061 raw=0.0203 t=103s *
[ep 45/80] H=6 lr=8.4e-04 loss=6.5062e-03 val_roll(multi)=0.0050 final=0.0079 start4=0.0050 raw=0.0122 t=126s *
[ep 50/80] H=6 lr=6.4e-04 loss=5.2161e-03 val_roll(multi)=0.0041 final=0.0066 start4=0.0041 raw=0.0091 t=150s *
[ep 55/80] H=7 lr=4.3e-04 loss=4.3681e-03 val_roll(multi)=0.0034 final=0.0055 start4=0.0035 raw=0.0098 t=176s *

Excellent progress! At ep55, val_roll multi = 0.0034, final = 0.0055. Compare baseline 0.0093 multi / 0.0176 final. This is already ~2.7x better on multi and ~3.2x on final. And it's still improving as LR decays and H increases. Let me wait for it to finish (ep80). The raw model is still noticeably worse than EMA (0.0098 vs 0.0034 at ep55), but as LR→0 it should converge. EMA is clearly the winner to save. Let me wait for completion.

Strong improvement , at ep55 EMA val rollout is 0.0034 (vs baseline 0.0093), and still dropping as the horizon curriculum and LR decay continue. Let me wait for it to finish.

bash

cd /app && sleep 140; tail -8 /app/logs_A.txt

output

[ep 40/80] H=5 lr=1.1e-03 loss=7.6924e-03 val_roll(multi)=0.0061 final=0.0095 start4=0.0061 raw=0.0203 t=103s *
[ep 45/80] H=6 lr=8.4e-04 loss=6.5062e-03 val_roll(multi)=0.0050 final=0.0079 start4=0.0050 raw=0.0122 t=126s *
[ep 50/80] H=6 lr=6.4e-04 loss=5.2161e-03 val_roll(multi)=0.0041 final=0.0066 start4=0.0041 raw=0.0091 t=150s *
[ep 55/80] H=7 lr=4.3e-04 loss=4.3681e-03 val_roll(multi)=0.0034 final=0.0055 start4=0.0035 raw=0.0098 t=176s *
[ep 60/80] H=7 lr=2.7e-04 loss=3.3427e-03 val_roll(multi)=0.0029 final=0.0047 start4=0.0030 raw=0.0043 t=202s *
[ep 65/80] H=8 lr=1.3e-04 loss=2.6157e-03 val_roll(multi)=0.0024 final=0.0040 start4=0.0026 raw=0.0060 t=233s *
[ep 70/80] H=9 lr=3.7e-05 loss=2.4206e-03 val_roll(multi)=0.0021 final=0.0035 start4=0.0023 raw=0.0025 t=264s *
[ep 75/80] H=9 lr=2.0e-06 loss=2.3671e-03 val_roll(multi)=0.0019 final=0.0032 start4=0.0021 raw=0.0018 t=296s *

At ep75, val_roll multi = 0.0019 EMA (raw 0.0018), final 0.0032. That's ~5x better than baseline (0.0093). Excellent. Let me wait for it to complete and write runA.pt.

bash

cd /app && sleep 30; tail -4 /app/logs_A.txt; echo "---EVAL runA---"; python3 /app/rollout_eval.py /app/outputs/runA.pt

output

[ep 70/80] H=9 lr=3.7e-05 loss=2.4206e-03 val_roll(multi)=0.0021 final=0.0035 start4=0.0023 raw=0.0025 t=264s *
[ep 75/80] H=9 lr=2.0e-06 loss=2.3671e-03 val_roll(multi)=0.0019 final=0.0032 start4=0.0021 raw=0.0018 t=296s *
[ep 80/80] H=10 lr=0.0e+00 loss=2.4066e-03 val_roll(multi)=0.0018 final=0.0030 start4=0.0021 raw=0.0018 t=330s *
[done] best val_roll=0.0018 wrote /app/outputs/runA.pt
---EVAL runA---
[val] start@4: rollout_rel_l2=0.0020 final=0.0033
[val] multi-start(10): rollout_rel_l2=0.0018 final=0.0030
[val] per-step(first): 0.001 0.001 0.001 0.001 0.001 0.001 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.003 0.003 0.003 0.003 0.003 0.003
[public_test] start@4: rollout_rel_l2=0.0023 final=0.0039
[public_test] multi-start(10): rollout_rel_l2=0.0015 final=0.0026
[public_test] per-step(first): 0.001 0.001 0.001 0.001 0.001 0.001 0.002 0.002 0.002 0.002 0.002 0.003 0.003 0.003 0.003 0.003 0.003 0.004 0.004 0.004

Run A results: - val start@4: rollout_rel_l2 = 0.0020, final = 0.0033 - val multi: 0.0018 / 0.0030 - public_test start@4: 0.0023 / 0.0039 - public_test multi: 0.0015 / 0.0026 Compared to baseline: - val start@4: baseline 0.0099 → runA 0.0020 (~5x better), final 0.0192 → 0.0033 (~5.8x) - public_test start@4: baseline 0.0082 → 0.0023, final 0.0149 → 0.0039 This is a huge improvement. The per-step error is now basically flat (0.001 → 0.003) instead of growing 0.003→0.019. The multi-step training clearly worked: it flattened the error growth curve. Now let me see if I can push further. Ideas: 1. Higher HMAX (train to full 20 steps to match eval horizon exactly). Currently HMAX=10. The eval is 20 steps. Training at H up to 20 might help the tail. But BPTT through 20 steps might be less stable; I have BPTT=4 truncation which keeps it stable. Let me try HMAX=20. 2. Longer training / more epochs at high H. 3. Tune noise, LR. Let me think about what matters most. The eval rolls 20 steps. Training with HMAX=10 already generalizes well to 20 (per-step stays low even beyond 10). But matching the horizon could help the later steps. Let me try HMAX=15 or 20. Also, I could increase epochs to give more time at each horizon. And maybe start the curriculum differently. But I should be careful about overfitting to val. The val and public_test both improved consistently, and these are exact symmetry augmentations + physically-grounded training, so I'm not overfitting to val specifically. The improvement is robust. Let me also reconsider: is there risk the grader evaluates differently (e.g., a specific start, or scores differently)? My model is good across all starts and both horizons, so it should be robust. Let me verify stability over a longer rollout (e.g., 40 steps) to ensure it doesn't blow up beyond 20 , in case the grader's "20 steps" or the model's stability matters. Let me check rollout to 40 steps. Let me also try a couple of config variations to squeeze more: - Run B: HMAX=20, EP=100, slightly more epochs at high H. Keep BPTT=4. - Maybe lower noise or higher noise. Let me first check long-horizon stability of runA, then launch run B. Let me check stability at longer horizons and also verify the model doesn't diverge.

Run A is a large improvement over baseline across **both** val and public_test: | metric (start@4) | baseline | run A | |---|---|---| | val rollout rel L2 | 0.0099 | **0.0020** | | val final-step | 0.0192 | **0.0033** | | public_test rollout | 0.0082 | **0.0023** | The per-step error curve is now essentially flat (0.001→0.003) instead of growing to 0.019 , multi-step training flattened the error accumulation exactly as intended. Let me verify the rollout stays stable well beyond 20 steps (robustness), then try to push further with a longer horizon curriculum.

bash

cd /app && python3 -c "
import json, torch, sys
sys.path.insert(0,'/app')
from candidate_model import build_model
from rollout_eval import rollout_metrics
cfg=json.load(open('/app/task_config.json'))
ck=torch.load('/app/outputs/runA.pt',weights_only=True)
m=build_model(cfg).cuda(); m.load_state_dict(ck['state_dict'])
for split in ['val','public_test']:
    tr=torch.load(f'/app/data/{split}/trajectories.pt',weights_only=True)['trajectories'].float()
    for H in [20,40,60]:
        r=rollout_metrics(m,tr,ck['feat_mean'],ck['feat_std'],H=H,starts=[4],device='cuda')
        print(f'{split} H={H}: roll={r[\"rollout_rel_l2\"]:.4f} final={r[\"final_step_rel_l2\"]:.4f}')
"

output

val H=20: roll=0.0020 final=0.0033
val H=40: roll=0.0037 final=0.0075
val H=60: roll=0.0063 final=0.0169
public_test H=20: roll=0.0023 final=0.0039
public_test H=40: roll=0.0042 final=0.0077
public_test H=60: roll=0.0058 final=0.0107

The model is stable well beyond 20 steps (at H=60, final only 0.017). No blow-up, which means the rollout is very stable. Good robustness. Now let me try to push further. Let me try: - Run B: HMAX=20 (match eval horizon), EP=100, to see if matching the horizon and more epochs helps the 20-step metric further. Let me also consider the possibility that with only single val set (32 traj) the differences at 0.0018 level are small. But public_test confirms. Let me try run B and compare. Actually, let me think about diminishing returns and token budget. Run A already gives ~5x improvement. That's a strong result. But let me try to improve further with a couple of targeted experiments, running them in background in parallel-ish (sequentially since one GPU but I can queue). Let me try Run B: HMAX=20, EP=100. And maybe a variant with pushforward mode to compare generalization. Let me also reconsider the loss. Currently rel L2 per step averaged. The eval metric is "rollout_rel_l2_score" , average over rollout. My training with equal weight per step matches. Good. Let me launch Run B with HMAX=20 and more epochs. I'll keep everything else same. This directly trains the exact eval horizon. Actually, I realize that training at larger H reduces the number of starting positions slightly but that's negligible (T=100). And BPTT=4 truncation keeps memory/gradient stable. Let me go. Let me run B.

The rollout is very stable even out to 60 steps (no blow-up) , good robustness margin beyond the 20-step eval. Now let me try to push further by matching the training horizon to the eval horizon (HMAX=20) with more epochs. I'll run it in the background.

bash

cd /app && nohup env EP=110 WARMUP=5 HMAX=20 MODE=bptt BPTT=4 NOISE=0.01 AUG=1 EMA=0.999 LR=2e-3 OUT=/app/outputs/runB.pt python3 train_improved.py > /app/logs_B.txt 2>&1 &
echo "launched B PID $!"; sleep 5; echo ok

output

launched B PID 247
ok

While run B trains, let me think about other potential improvements and the grading. Let me verify the output contract: model.pt loadable with weights_only=True, containing state_dict matching build_model(task_config), and feat_mean/feat_std as float scalars. My save does torch.save({"state_dict": sd, "feat_mean": float, "feat_std": float}, OUT). feat_mean/feat_std are Python floats. Good. state_dict is from ema/model which is build_model(cfg). Let me double check that loading with weights_only=True works and that state_dict keys match a fresh build_model. Let me verify the checkpoint loads correctly in a clean way (weights_only=True) and keys match. Let me test on runA.pt.

While run B trains, let me verify the output contract is satisfied exactly , the checkpoint must load with `weights_only=True` and its `state_dict` must match a fresh `build_model(task_config)`.

bash

cd /app && python3 -c "
import json, torch, sys
sys.path.insert(0,'/app')
from candidate_model import build_model
cfg=json.load(open('/app/task_config.json'))
ck=torch.load('/app/outputs/runA.pt', weights_only=True)
print('keys:', list(ck.keys()))
print('feat_mean type:', type(ck['feat_mean']), ck['feat_mean'])
print('feat_std type:', type(ck['feat_std']), ck['feat_std'])
m=build_model(cfg)
missing, unexpected = m.load_state_dict(ck['state_dict'], strict=True)
print('strict load OK; missing:', missing, 'unexpected:', unexpected)
print('num params:', sum(p.numel() for p in m.parameters()))
"

output

keys: ['state_dict', 'feat_mean', 'feat_std']
feat_mean type: <class 'float'> -3.4108758040218845e-09
feat_std type: <class 'float'> 1.1599736213684082
strict load OK; missing: [] unexpected: []
num params: 71041

The contract is satisfied: keys are correct, feat_mean/feat_std are Python floats, strict load works with no missing/unexpected keys. Model has 71041 params. Now let me think about whether the grader might feed the window WITHOUT normalizing (i.e., expects the model to handle raw input). Let me reconsider. The contract says feat_mean, feat_std are "float scalars for input/output standardization." This strongly implies the grader normalizes input and denormalizes output using these. The baseline train.py does exactly this. And the evaluation harness is "hidden" but the contract format matches the baseline's convention. So I'm confident. But wait , there's an important subtlety. What if the grader normalizes the input window, runs the model, and then for the NEXT step, it feeds the raw predicted frame (denormalized) back, re-normalizing? That's what my rollout_eval does: I keep everything in normalized space during rollout (window stays normalized, output appended directly). Let me reconsider the grader's likely loop: Likely grader loop: ``` window = raw last k frames # physical for step in range(20): x = (window - feat_mean)/feat_std y = model(x) # normalized prediction pred = y*feat_std + feat_mean # physical window = concat(window[1:], pred) save pred ``` This is equivalent to my normalized-space rollout because normalization is affine and consistent. In my rollout_eval, I keep win normalized and append out (normalized) directly , same thing. So it matches. Good. Now, is there any chance the grader does NOT normalize (feeds raw window to model)? Then my model, trained on normalized input, would fail. But the contract explicitly provides feat_mean/feat_std "for input/output standardization," so the grader must use them. The baseline also relies on this. I'll trust it. Let me also make sure feat_mean and feat_std are plain Python float (not numpy/tensor) , yes, confirmed type float. Now let me continue thinking about improvements while B runs. Let me check B's progress. Actually, let me also consider: should I be worried that my EMA state_dict includes the non-persistent buffer `_grid`? The buffer is registered persistent=False, so it's NOT in state_dict. Let me verify that strict load works , yes it did (missing: [], unexpected: []). Good, because build_model creates the _grid buffer fresh (non-persistent, recomputed). Wait, strict load with missing/unexpected both empty means state_dict doesn't contain _grid and model doesn't expect it in state_dict (since non-persistent). Good. Let me check B progress.

Contract verified: keys correct, `feat_mean`/`feat_std` are Python floats, and `state_dict` loads strictly into a fresh `build_model(task_config)` (the non-persistent `_grid` buffer is correctly excluded). Let me check on run B's progress.

bash

cd /app && sleep 150; tail -8 /app/logs_B.txt

output

[ep 10/110] H=2 lr=2.0e-03 loss=1.9054e-02 val_roll(multi)=0.0156 final=0.0256 start4=0.0180 raw=0.0505 t=20s *
[ep 15/110] H=3 lr=2.0e-03 loss=2.1837e-02 val_roll(multi)=0.0134 final=0.0233 start4=0.0155 raw=0.0742 t=31s *
[ep 20/110] H=4 lr=1.9e-03 loss=1.7769e-02 val_roll(multi)=0.0103 final=0.0173 start4=0.0117 raw=0.0485 t=46s *
[ep 25/110] H=4 lr=1.8e-03 loss=1.2020e-02 val_roll(multi)=0.0092 final=0.0148 start4=0.0101 raw=0.0276 t=63s *
[ep 30/110] H=5 lr=1.7e-03 loss=1.3839e-02 val_roll(multi)=0.0078 final=0.0126 start4=0.0082 raw=0.0217 t=83s *
[ep 35/110] H=6 lr=1.6e-03 loss=1.0894e-02 val_roll(multi)=0.0073 final=0.0113 start4=0.0075 raw=0.0214 t=107s *
[ep 40/110] H=7 lr=1.5e-03 loss=1.0229e-02 val_roll(multi)=0.0070 final=0.0110 start4=0.0071 raw=0.0165 t=134s *
[ep 45/110] H=8 lr=1.3e-03 loss=9.8327e-03 val_roll(multi)=0.0063 final=0.0099 start4=0.0062 raw=0.0218 t=164s *

Run B is progressing. At ep45 (H=8), val_roll multi = 0.0063. At the same relative progress (ep45/110 ≈ 41%), run A (ep45/80 ≈ 56% but H=6) was at 0.0050. Hmm, B is slower because it spends more epochs ramping to H=20 and LR is still high. It will need the LR decay phase to converge. Let me let it run. The question is whether ending at H=20 gives a better final than run A's H=10. Let me wait for B to get into the LR decay phase (ep 70+) to judge. Meanwhile, let me think about whether there's a smarter approach. One consideration: the per-step error in run A grows from 0.001 to 0.003 over 20 steps , very flat. The remaining error is dominated by early steps (the one-step accuracy floor ~0.001) plus slow growth. To reduce further, I'd need better one-step accuracy AND controlled growth. Run A's one-step is ~0.001 (from per-step[0]=0.001). The baseline one-step was 0.0028. So multi-step training also improved one-step? Actually per-step[0] of 0.001 vs baseline's 0.003 , yes multistep + aug + more epochs improved even one-step. Could a larger model help? No, architecture is fixed. Could training longer at the one-step level first (more warmup at H=1) help the floor? Possibly. But the gains are small. Let me consider the loss function more. The metric is rel L2. I'm using rel L2. Good match. Adding a spectral loss (SPEC_W) to better capture high-frequency content might help chaotic detail, but the error is already tiny. Let me not over-engineer. Let me also double-check that my augmentation (reflect+negate, shift) is actually helping and not hurting. I could do an ablation, but given both val and public_test improved a lot and these are exact symmetries, I'm confident they help generalization. I'll keep them. Let me wait for B and compare final. Let me also prepare to pick the best checkpoint and copy to /app/outputs/model.pt. Let me wait more.

Run B (horizon ramping to 20) is progressing but slower per-epoch since it spends more time at large H; it needs its LR-decay phase to converge. Let me wait for it to reach the decay phase before judging.

bash

cd /app && sleep 180; tail -8 /app/logs_B.txt

output

[ep 35/110] H=6 lr=1.6e-03 loss=1.0894e-02 val_roll(multi)=0.0073 final=0.0113 start4=0.0075 raw=0.0214 t=107s *
[ep 40/110] H=7 lr=1.5e-03 loss=1.0229e-02 val_roll(multi)=0.0070 final=0.0110 start4=0.0071 raw=0.0165 t=134s *
[ep 45/110] H=8 lr=1.3e-03 loss=9.8327e-03 val_roll(multi)=0.0063 final=0.0099 start4=0.0062 raw=0.0218 t=164s *
[ep 50/110] H=9 lr=1.1e-03 loss=8.6265e-03 val_roll(multi)=0.0058 final=0.0091 start4=0.0058 raw=0.0113 t=197s *
[ep 55/110] H=10 lr=9.8e-04 loss=7.6814e-03 val_roll(multi)=0.0051 final=0.0081 start4=0.0051 raw=0.0102 t=230s *
[ep 60/110] H=11 lr=8.1e-04 loss=1.0660e-02 val_roll(multi)=0.0045 final=0.0071 start4=0.0045 raw=0.0100 t=265s *
[ep 65/110] H=12 lr=6.4e-04 loss=6.3127e-03 val_roll(multi)=0.0039 final=0.0062 start4=0.0039 raw=0.0091 t=303s *
[ep 70/110] H=13 lr=4.9e-04 loss=5.3266e-03 val_roll(multi)=0.0034 final=0.0054 start4=0.0034 raw=0.0061 t=342s *

At ep70 (H=13), val_roll multi = 0.0034. Run A at the end (ep80) was 0.0018. B still has 40 epochs to go with LR decaying and H going to 20. Let me wait for it to finish. It's at t=342s, epochs take ~7s each now at high H, 40 more epochs ≈ 280s. Let me wait. Let me think about whether B will beat A. B is ending at H=20 which matches eval horizon exactly. A ended at H=10 and got 0.0018. B's trajectory is tracking slightly behind A at comparable points but it has more epochs total. The key is the final convergence with low LR at high H. Let me wait ~250s more.

bash

cd /app && sleep 250; tail -9 /app/logs_B.txt

output

[ep 55/110] H=10 lr=9.8e-04 loss=7.6814e-03 val_roll(multi)=0.0051 final=0.0081 start4=0.0051 raw=0.0102 t=230s *
[ep 60/110] H=11 lr=8.1e-04 loss=1.0660e-02 val_roll(multi)=0.0045 final=0.0071 start4=0.0045 raw=0.0100 t=265s *
[ep 65/110] H=12 lr=6.4e-04 loss=6.3127e-03 val_roll(multi)=0.0039 final=0.0062 start4=0.0039 raw=0.0091 t=303s *
[ep 70/110] H=13 lr=4.9e-04 loss=5.3266e-03 val_roll(multi)=0.0034 final=0.0054 start4=0.0034 raw=0.0061 t=342s *
[ep 75/110] H=13 lr=3.7e-04 loss=4.8277e-03 val_roll(multi)=0.0028 final=0.0046 start4=0.0030 raw=0.0056 t=384s *
[ep 80/110] H=14 lr=2.4e-04 loss=3.6532e-03 val_roll(multi)=0.0024 final=0.0040 start4=0.0026 raw=0.0031 t=430s *
[ep 85/110] H=15 lr=1.4e-04 loss=3.2449e-03 val_roll(multi)=0.0021 final=0.0035 start4=0.0023 raw=0.0031 t=477s *
[ep 90/110] H=16 lr=5.7e-05 loss=2.9984e-03 val_roll(multi)=0.0019 final=0.0032 start4=0.0021 raw=0.0022 t=526s *
[ep 95/110] H=17 lr=1.0e-05 loss=2.9449e-03 val_roll(multi)=0.0018 final=0.0030 start4=0.0020 raw=0.0017 t=578s *

At ep95, B val_roll multi = 0.0018, same as A's final. B still has 15 epochs at very low LR and H→20. Let me wait for it to finish.

bash

cd /app && sleep 130; tail -5 /app/logs_B.txt; echo "---EVAL runB---"; python3 /app/rollout_eval.py /app/outputs/runB.pt 2>/dev/null | grep -E "start@4|multi-start"

output

[ep 95/110] H=17 lr=1.0e-05 loss=2.9449e-03 val_roll(multi)=0.0018 final=0.0030 start4=0.0020 raw=0.0017 t=578s *
[ep 100/110] H=18 lr=0.0e+00 loss=2.9943e-03 val_roll(multi)=0.0017 final=0.0029 start4=0.0019 raw=0.0017 t=631s *
[ep 105/110] H=19 lr=0.0e+00 loss=3.0371e-03 val_roll(multi)=0.0017 final=0.0028 start4=0.0019 raw=0.0017 t=686s
[ep 110/110] H=20 lr=0.0e+00 loss=3.1167e-03 val_roll(multi)=0.0017 final=0.0028 start4=0.0019 raw=0.0017 t=741s *
[done] best val_roll=0.0017 wrote /app/outputs/runB.pt
---EVAL runB---
[val] start@4: rollout_rel_l2=0.0019 final=0.0032
[val] multi-start(10): rollout_rel_l2=0.0017 final=0.0028
[public_test] start@4: rollout_rel_l2=0.0019 final=0.0033
[public_test] multi-start(10): rollout_rel_l2=0.0014 final=0.0025

Run B results: - val start@4: 0.0019 / 0.0032 - val multi: 0.0017 / 0.0028 - public_test start@4: 0.0019 / 0.0033 - public_test multi: 0.0014 / 0.0025 Run A: - val start@4: 0.0020 / 0.0033 - public_test start@4: 0.0023 / 0.0039 So B is marginally better than A, especially on public_test start@4 (0.0019 vs 0.0023). The difference is small but consistent. B trained to the full horizon 20 and it paid off slightly, particularly improving the public_test generalization. Both are ~5x better than baseline. The improvements are getting marginal now. Let me decide on the final approach. Given diminishing returns, let me consider whether to do one more run combining the best ideas, or just pick B. Let me think about variance. The differences between A and B at the 0.0017-0.0023 level are small and could be partly noise (val has 32 trajectories, public_test 16). But B is better or equal on all four metrics. B seems the safer, better choice. Let me consider one more improvement attempt: an ensemble? No , the grader loads a single state_dict matching build_model. Can't ensemble (one model only). Could I do a longer/more refined run? Let me consider a "Run C" that: - Uses HMAX=20, but more epochs (e.g., 140) and maybe a lower peak LR (1.5e-3) for stability, plus slightly higher noise or a longer low-LR tail. Actually, the marginal gains suggest I'm near the floor for this architecture. The one-step error floor (~0.001) times accumulation sets the limit. Let me think about whether I can reduce the one-step floor. The one-step rel L2 is ~0.001 (per-step[0]). Is that limited by model capacity (width 32, modes 16) or training? KS solutions on 128 grid , modes 16 means the model only uses the lowest 16 Fourier modes in spectral convs. But KS with L=22 has most energy in low modes (the characteristic wavelength ~2π/q where q~1/sqrt(2)). Let me check the energy spectrum to see if modes=16 is enough. Actually modes=16 is fixed by config; I can't change it. So capacity is what it is. Let me consider: is there benefit to even more epochs / different curriculum? The loss is still ~3e-3 (rel L2 over 20 steps averaged). The val is 0.0017. These are close, meaning not much overfitting and not much more to squeeze via longer training. Let me try one more targeted run (Run C) to see if I can get a bit more, with a refined recipe: - EP=130, HMAX=20, reach HMAX earlier (so more epochs spent at full horizon with decaying LR), slightly lower LR=1.5e-3, and maybe a touch more noise (0.015) for robustness. Also extend the low-LR fine-tuning at H=20. Actually, a cleaner idea: reach HMAX by ~60% of training, then spend the last 40% at H=20 with cosine-decaying LR. My current curr_H ramps linearly to HMAX at the final epoch, so the model only sees H=20 at the very end. Better to reach HMAX earlier and fine-tune at full horizon with low LR. Let me modify the curriculum to reach HMAX at, say, 70% of epochs. Let me add a config HFRAC = fraction of (post-warmup) epochs over which H ramps to HMAX (default 1.0). Set HFRAC=0.7 so it reaches H=20 at 70% and stays there for the final 30% while LR decays to 0. Let me implement that and run Run C. This could meaningfully help the tail of the rollout since the model fine-tunes at the exact eval horizon with low LR. Let me modify curr_H.

Run B (horizon→20) edges out run A, especially on public_test start@4 (0.0019 vs 0.0023), and is best-or-equal on all four metrics: | metric | baseline | run A (H→10) | run B (H→20) | |---|---|---|---| | val start@4 rollout | 0.0099 | 0.0020 | **0.0019** | | val start@4 final | 0.0192 | 0.0033 | **0.0032** | | public_test start@4 rollout | 0.0082 | 0.0023 | **0.0019** | One inefficiency: my curriculum only reaches H=20 at the very last epoch. It would be better to reach the full horizon earlier and then *fine-tune at H=20 while the LR decays to zero*. Let me add a knob for that and run a refined "Run C".

str_replace_based_edit_tool

command: str_replace
path: /app/train_improved.py
old_str:     def curr_H(ep):
        # ramp horizon 1 -> HMAX over training (after warmup)
        if ep < WARMUP: return 1
        frac = (ep - WARMUP) / max(1, (EPOCHS - WARMUP))
        return int(1 + round(frac * (HMAX - 1)))
new_str:     HFRAC = get("HFRAC", 1.0, float)  # fraction of post-warmup epochs to reach HMAX
    def curr_H(ep):
        # ramp horizon 1 -> HMAX over first HFRAC of (post-warmup) epochs, then hold
        if ep < WARMUP: return 1
        frac = (ep - WARMUP) / max(1, (EPOCHS - WARMUP))
        frac = min(1.0, frac / max(1e-8, HFRAC))
        return int(1 + round(frac * (HMAX - 1)))

output

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

Now let me launch Run C: reach HMAX=20 at 65% of training, then fine-tune at H=20 for the last 35% as LR decays. Use EP=130, LR=2e-3, HFRAC=0.65. Keep other settings. Let me also try slightly more epochs to allow a longer fine-tune tail. Let me launch it.

bash

cd /app && nohup env EP=130 WARMUP=5 HMAX=20 HFRAC=0.6 MODE=bptt BPTT=4 NOISE=0.01 AUG=1 EMA=0.999 LR=2e-3 OUT=/app/outputs/runC.pt python3 train_improved.py > /app/logs_C.txt 2>&1 &
echo "launched C PID $!"; sleep 3; echo ok

output

launched C PID 326
ok

While C trains (will take ~12-13 min given B took ~740s for 110 epochs, C is 130 epochs with more time at high H, maybe ~900-1000s), let me think about whether there are other robustness concerns. One important thing: I should make sure my final model.pt is the best checkpoint. Currently runB.pt is my best. Let me plan: after C finishes, compare A/B/C on both val and public_test, pick the best, and copy to /app/outputs/model.pt. But I should be careful about model selection: picking based on val could overfit to val's 32 trajectories. public_test is another held-out set (16 traj). The true grader uses a hidden set. To avoid overfitting model selection, I should pick the config that's robustly best across both val and public_test, not micro-optimize. A, B, C are all strong; B and C (full horizon) are slightly better. I'll pick based on a combined criterion (val + public_test) but they're all so close it won't matter much. Let me also reconsider: the grader might use a different rollout start or might compute the score over a specific window. My model is good everywhere (multi-start and start@4 both excellent, stable to 60 steps). So robust. Let me also reconsider the score definition. "rollout_rel_l2_score" , higher is better presumably (it's a "score"). It might be computed as something like max(0, 1 - rel_l2/ref) or 1/(1+rel_l2) or a normalized comparison vs baseline. In any case, lower rel_l2 → higher score. My model minimizes rel_l2. Good. Now, let me think: is there any risk my aggressive training hurt some aspect the grader cares about? E.g., secondary metrics: rollout_rmse_score, final_step_rel_l2_score. My model has low final_step error too (0.0028 val). RMSE is just unnormalized L2, also minimized. So all metrics improved. Let me also double check there are no NaNs/Infs in the saved model and that a CPU load works (grader might run on CPU or GPU). Let me test CPU inference quickly on runB. Let me verify CPU load + forward works (in case grader uses CPU), and check for NaNs.

Run C is training. While it runs, let me make sure the checkpoint also works on CPU (the grader's device is unknown) and is free of NaNs/Infs.

bash

cd /app && python3 -c "
import json, torch, sys
sys.path.insert(0,'/app')
from candidate_model import build_model
cfg=json.load(open('/app/task_config.json'))
ck=torch.load('/app/outputs/runB.pt', weights_only=True, map_location='cpu')
m=build_model(cfg); m.load_state_dict(ck['state_dict']); m.eval()
# check finite
bad=any((not torch.isfinite(p).all()) for p in m.parameters())
print('any non-finite params:', bad)
x=torch.randn(2,4,128)
xn=(x-ck['feat_mean'])/ck['feat_std']
with torch.no_grad(): y=m(xn)
print('cpu forward out shape:', tuple(y.shape), 'finite:', torch.isfinite(y).all().item())
"

output

any non-finite params: False
cpu forward out shape: (2, 1, 128) finite: True

Good. CPU load and forward work, output shape [2,1,128] correct, all finite. Let me now wait for run C. Let me check progress periodically. Let me sleep and check. While waiting, let me reconsider if there's anything that could give a bigger jump. Actually, one more idea worth testing: the current model selection saves the best EMA checkpoint during training based on val multi-start rollout. That's good. But I evaluate only every 5 epochs. Fine. Another thought: the grader's rollout uses the model's own predictions fed back. My training with BPTT and the flat error curve already handles this. I think the main remaining question is just picking the best of A/B/C. Let me wait for C. Let me also prepare a final validation script that evaluates a given checkpoint thoroughly (multi-start, start@4, H=20) on both val and public_test, to make the final decision. Let me check C progress.

CPU inference works and all params/outputs are finite. Let me monitor run C's progress.

bash

cd /app && sleep 240; tail -7 /app/logs_C.txt

output

[ep 20/130] H=5 lr=1.9e-03 loss=1.4939e-02 val_roll(multi)=0.0105 final=0.0167 start4=0.0118 raw=0.0250 t=50s *
[ep 25/130] H=6 lr=1.9e-03 loss=2.0856e-02 val_roll(multi)=0.0087 final=0.0141 start4=0.0095 raw=0.0529 t=71s *
[ep 30/130] H=7 lr=1.8e-03 loss=1.3009e-02 val_roll(multi)=0.0080 final=0.0126 start4=0.0084 raw=0.0221 t=96s *
[ep 35/130] H=8 lr=1.7e-03 loss=1.2093e-02 val_roll(multi)=0.0079 final=0.0125 start4=0.0082 raw=0.0211 t=126s *
[ep 40/130] H=10 lr=1.6e-03 loss=1.2691e-02 val_roll(multi)=0.0072 final=0.0112 start4=0.0074 raw=0.0162 t=159s *
[ep 45/130] H=11 lr=1.5e-03 loss=1.1076e-02 val_roll(multi)=0.0068 final=0.0106 start4=0.0070 raw=0.0126 t=195s *
[ep 50/130] H=12 lr=1.3e-03 loss=1.6040e-02 val_roll(multi)=0.0062 final=0.0097 start4=0.0065 raw=0.0216 t=236s *

C reaches H=20 at 60% of (130-5)+5 ≈ ep 80. At ep50 (H=12), it's at 0.0062. It's on a similar track to B. The fine-tuning tail (ep 80-130 at H=20 with decaying LR) should help. Let me wait for it to reach the tail. Let me wait ~300s more.

bash

cd /app && sleep 300; tail -7 /app/logs_C.txt

output

[stderr]
Shell command timed out.
Tool interaction exceeded 300 seconds.

bash

restart: true

output

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

bash

cd /app && tail -8 /app/logs_C.txt

output

[ep 45/130] H=11 lr=1.5e-03 loss=1.1076e-02 val_roll(multi)=0.0068 final=0.0106 start4=0.0070 raw=0.0126 t=195s *
[ep 50/130] H=12 lr=1.3e-03 loss=1.6040e-02 val_roll(multi)=0.0062 final=0.0097 start4=0.0065 raw=0.0216 t=236s *
[ep 55/130] H=13 lr=1.2e-03 loss=9.9365e-03 val_roll(multi)=0.0057 final=0.0088 start4=0.0060 raw=0.0129 t=278s *
[ep 60/130] H=15 lr=1.1e-03 loss=1.6372e-02 val_roll(multi)=0.0050 final=0.0078 start4=0.0053 raw=0.0176 t=329s *
[ep 65/130] H=16 lr=9.0e-04 loss=1.3259e-02 val_roll(multi)=0.0047 final=0.0074 start4=0.0050 raw=0.0123 t=382s *
[ep 70/130] H=17 lr=7.5e-04 loss=1.2486e-02 val_roll(multi)=0.0043 final=0.0071 start4=0.0047 raw=0.0121 t=434s *
[ep 75/130] H=18 lr=6.1e-04 loss=1.4380e-02 val_roll(multi)=0.0040 final=0.0067 start4=0.0044 raw=0.0152 t=488s *
[ep 80/130] H=20 lr=4.6e-04 loss=1.3940e-02 val_roll(multi)=0.0039 final=0.0064 start4=0.0044 raw=0.0076 t=550s *

At ep80, C reaches H=20 with val_roll 0.0039, lr=4.6e-4. Now it has 50 more epochs (ep80-130) at H=20 with LR decaying from 4.6e-4 to 0. This is the fine-tuning tail. Let me wait for completion. The process is running in background (PID 326). Let me check in shorter increments to avoid the 300s timeout. Each check: sleep 200.

Run C reached H=20 at ep80 (val 0.0039) and is now in the low-LR fine-tuning tail at the full eval horizon. I'll check in shorter intervals to avoid the shell timeout.

bash

cd /app && sleep 200; tail -5 /app/logs_C.txt

output

[ep 60/130] H=15 lr=1.1e-03 loss=1.6372e-02 val_roll(multi)=0.0050 final=0.0078 start4=0.0053 raw=0.0176 t=329s *
[ep 65/130] H=16 lr=9.0e-04 loss=1.3259e-02 val_roll(multi)=0.0047 final=0.0074 start4=0.0050 raw=0.0123 t=382s *
[ep 70/130] H=17 lr=7.5e-04 loss=1.2486e-02 val_roll(multi)=0.0043 final=0.0071 start4=0.0047 raw=0.0121 t=434s *
[ep 75/130] H=18 lr=6.1e-04 loss=1.4380e-02 val_roll(multi)=0.0040 final=0.0067 start4=0.0044 raw=0.0152 t=488s *
[ep 80/130] H=20 lr=4.6e-04 loss=1.3940e-02 val_roll(multi)=0.0039 final=0.0064 start4=0.0044 raw=0.0076 t=550s *

The log hasn't advanced past ep80 in the last 200s? That's odd. t=550s at ep80. The process may have slowed because H=20 epochs are more expensive (20 sequential model calls per batch). Let me check if the process is still alive and how long each epoch at H=20 takes. Actually wait, the last printed is ep80 at t=550s. At H=20, each epoch does ~136 batches × 20 model calls. That's 2720 model calls per epoch on batch 128. Should be maybe ~8-10s/epoch. But evals only happen every 5 epochs. So between ep80 and ep85 it runs 5 epochs (~40-50s) then prints. 200s should be enough for ~4 eval points. But the log still shows ep80. Let me check if the process is alive.

bash

cd /app && ps aux | grep train_improved | grep -v grep | head; echo "---"; nvidia-smi --query-gpu=utilization.gpu,memory.used --format=csv,noheader 2>/dev/null

output

---
100 %, 56278 MiB

Hmm, GPU is at 100% util and 56GB used, but `ps` shows no train_improved process? That's strange. Wait, the grep returned nothing for train_improved but GPU is busy at 56GB. Let me check all python processes. Maybe the process name shows differently. Wait, 56GB GPU memory used is a LOT for this tiny model. That suggests something is wrong , maybe memory is being accumulated. Actually, maybe there are multiple leftover processes, or the process got detached from this shell after restart and ps can't see it the same way. Let me check more broadly.

bash

cd /app && ps aux | grep -i python | grep -v grep; echo "---all procs by this user---"; ps -ef | grep -v grep | grep -E "python|train" | head; echo "---nvidia procs---"; nvidia-smi --query-compute-apps=pid,used_memory --format=csv 2>/dev/null

output

root        15  0.0  0.0  10960  4704 ?        S    06:28   0:00 /bin/sh -c /opt/h2t-venv/bin/python3 /mcp_server/server.py
root        16  0.0  0.2 222504 68552 ?        Sl   06:28   0:00 /opt/h2t-venv/bin/python3 /mcp_server/server.py
h2tagent   393  0.0  0.0 102276 21524 ?        Sl   07:02   0:00 python -c import sys; sys.path.insert(0, "/tmp/line_io_root_pehac"); import runpy; runpy.run_module("line_io_tools._shell_line_io_repl", run_name="__main__", alter_sys=True) --use-redirected-stdin --env null
---all procs by this user---
root        15     1  0 06:28 ?        00:00:00 /bin/sh -c /opt/h2t-venv/bin/python3 /mcp_server/server.py
root        16    15  0 06:28 ?        00:00:00 /opt/h2t-venv/bin/python3 /mcp_server/server.py
h2tagent   393     1  0 07:02 ?        00:00:00 python -c import sys; sys.path.insert(0, "/tmp/line_io_root_pehac"); import runpy; runpy.run_module("line_io_tools._shell_line_io_repl", run_name="__main__", alter_sys=True) --use-redirected-stdin --env null
---nvidia procs---
pid, used_gpu_memory [MiB]

The train_improved.py process (PID 326) is gone , it was killed when the shell/tool was restarted (it was a child of the previous shell). The nohup should have protected it, but when the tool "died" and restarted, the process group may have been terminated. The GPU shows 100% util and 56GB used but no compute-apps listed , that's likely a stale/transient reading or another container sharing the GPU. Actually `nvidia-smi --query-compute-apps` shows nothing, so no process of ours is using it. The 56GB/100% might be from another tenant on a shared GPU, or a stale reading. Anyway, run C died at ep80. But importantly, the "best" checkpoint logic saves only at the END (torch.save happens after the loop). So runC.pt was NOT written (the loop didn't finish). Let me verify. Actually, my script only saves at the very end. So if C was killed at ep80, runC.pt doesn't exist or is stale. Let me check. This is a problem with my approach: I should checkpoint the best model periodically, not just at the end, so a killed run still yields a usable artifact. Let me check what we have. The good news: runB.pt is complete and excellent (0.0017 val multi, 0.0019 start@4). I can use that as my final. But let me see if I can still get C's improvement. Let me check files first.

The background run C was killed when the shell restarted (nohup didn't survive the tool restart), and my script only saves at the very end , so runC.pt wasn't written. Let me check what checkpoints I have; runB.pt is complete and excellent regardless.

bash

cd /app && ls -la outputs/; echo "---GPU now---"; nvidia-smi --query-gpu=utilization.gpu,memory.used --format=csv,noheader

output

total 2164
drwxr-sr-x 2 h2tagent h2tagent   4096 Jun 20 06:52 .
drwxrws--- 1 h2tagent h2tagent   4096 Jun 20 06:53 ..
-rw-r--r-- 1 h2tagent h2tagent 551860 Jun 20 06:31 model.pt
-rw-r--r-- 1 h2tagent h2tagent 551838 Jun 20 06:39 runA.pt
-rw-r--r-- 1 h2tagent h2tagent 551838 Jun 20 06:52 runB.pt
-rw-r--r-- 1 h2tagent h2tagent 551860 Jun 20 06:33 smoke.pt
---GPU now---
100 %, 57658 MiB

As expected, no runC.pt. The GPU is at 100% util with 57GB , that must be another tenant sharing the physical GPU (since no compute-apps from us). That could slow my runs but shouldn't break them. Let me confirm I can still run on the GPU. Important lesson: I need to (1) make training robust to being killed by saving best checkpoint periodically, and (2) run within the 300s tool limit or use a robust background mechanism that survives. Let me improve the script to save the best checkpoint periodically (every eval). Then even if killed, I keep the best. Also, I should use `setsid` / proper detachment so the process survives shell restarts, and write a PID file. Actually, the cleanest approach given the 300s tool timeout: run training in background with setsid and nohup so it fully detaches, and poll with short sleeps. Let me also add periodic best-checkpoint saving. But first, let me reconsider whether I even need run C. Run B is already excellent and complete. The expected gain from C's fine-tuning tail is marginal (maybe 0.0017→0.0015ish). Given token/time budget, I could just finalize with B. But let me try to get a bit more with a robust run C, since the fine-tune-at-full-horizon idea is sound and C was tracking to beat B (at ep80 it was at 0.0039 with 50 low-LR epochs left; B at comparable H=20 start was similar and ended at 0.0017). Actually, C at ep80 (0.0039) vs B at ep80 (0.0024, but B was at H=14 then). Hard to compare directly. Let me just do a robust run and compare final numbers. Let me modify the script to: 1. Save best checkpoint to OUT every time we find a new best (not just at end). This makes it robust to kills. Let me edit.

Two lessons: the GPU now shows another tenant at 100%/57GB (not ours), and my script only saves at the end , so a killed run loses everything. Run B is complete and excellent, but let me make training **robust to interruראtion by saving the best checkpoint incrementally**, then relaunch the refined run C fully detached so it survives.

str_replace_based_edit_tool

command: str_replace
path: /app/train_improved.py
old_str:             cand_val, cand_sd = (m["rollout_rel_l2"], ema.state_dict()) if m["rollout_rel_l2"] <= mr["rollout_rel_l2"] else (mr["rollout_rel_l2"], model.state_dict())
            if cand_val < best["val"]:
                best = {"val": cand_val, "sd": copy.deepcopy(cand_sd)}
                tag = " *"
new_str:             cand_val, cand_sd = (m["rollout_rel_l2"], ema.state_dict()) if m["rollout_rel_l2"] <= mr["rollout_rel_l2"] else (mr["rollout_rel_l2"], model.state_dict())
            if cand_val < best["val"]:
                best = {"val": cand_val, "sd": copy.deepcopy(cand_sd)}
                tag = " *"
                Path(OUT).parent.mkdir(parents=True, exist_ok=True)
                torch.save({"state_dict": best["sd"], "feat_mean": feat_mean, "feat_std": feat_std}, OUT)

output

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

bash

cd /app && setsid bash -c 'env EP=130 WARMUP=5 HMAX=20 HFRAC=0.6 MODE=bptt BPTT=4 NOISE=0.01 AUG=1 EMA=0.999 LR=2e-3 OUT=/app/outputs/runC.pt python3 train_improved.py > /app/logs_C.txt 2>&1' < /dev/null &
disown; echo "relaunched C detached"; sleep 20; tail -3 /app/logs_C.txt

output

relaunched C detached
[ep 1/130] H=1 lr=4.0e-04 loss=6.3677e-01 val_roll(multi)=0.2385 final=0.4255 start4=0.2817 raw=0.1708 t=4s *
[ep 2/130] H=1 lr=8.0e-04 loss=2.0477e-02 val_roll(multi)=0.0278 final=0.0503 start4=0.0327 raw=0.0507 t=7s *
[ep 5/130] H=1 lr=2.0e-03 loss=3.2414e-02 val_roll(multi)=0.0269 final=0.0496 start4=0.0331 raw=0.1814 t=12s *

Good, C relaunched and detached (using setsid + disown so it survives shell restarts). It's training and saving the best to runC.pt incrementally now. Let me verify it's truly detached by checking the process exists. Let me poll in short intervals. Let me wait and check progress. I'll use ~200s sleeps to stay under the 300s limit.

Run C relaunched fully detached (via `setsid`/`disown`) and now saves its best checkpoint incrementally. Let me verify it's running independently and let it progress.

bash

cd /app && ps -ef | grep train_improved | grep -v grep | head -2; echo "---"; sleep 190; tail -5 /app/logs_C.txt

output

h2tagent   475   473  0 07:06 ?        00:00:00 bash -c env EP=130 WARMUP=5 HMAX=20 HFRAC=0.6 MODE=bptt BPTT=4 NOISE=0.01 AUG=1 EMA=0.999 LR=2e-3 OUT=/app/outputs/runC.pt python3 train_improved.py > /app/logs_C.txt 2>&1
h2tagent   476   475 98 07:06 ?        00:00:22 python3 train_improved.py
---
[ep 25/130] H=6 lr=1.9e-03 loss=2.0856e-02 val_roll(multi)=0.0087 final=0.0141 start4=0.0095 raw=0.0529 t=82s *
[ep 30/130] H=7 lr=1.8e-03 loss=1.3009e-02 val_roll(multi)=0.0080 final=0.0126 start4=0.0084 raw=0.0221 t=109s *
[ep 35/130] H=8 lr=1.7e-03 loss=1.2093e-02 val_roll(multi)=0.0079 final=0.0125 start4=0.0082 raw=0.0211 t=136s *
[ep 40/130] H=10 lr=1.6e-03 loss=1.2691e-02 val_roll(multi)=0.0072 final=0.0112 start4=0.0074 raw=0.0162 t=168s *
[ep 45/130] H=11 lr=1.5e-03 loss=1.1076e-02 val_roll(multi)=0.0068 final=0.0106 start4=0.0070 raw=0.0126 t=205s *

C is running detached (PID 476, parent 475). Progress matches the earlier run (same seed). At ep45 it's at 0.0068. It now saves best incrementally to runC.pt. Let me continue waiting. The fine-tune tail (ep80-130) is what I'm waiting for. Let me poll again. While waiting, let me think about the final selection and whether I should also run an ablation to confirm augmentation helps. But token budget is a concern. I'll focus on getting C done and picking the best. Let me wait ~200s.

Run C is confirmed running detached (PID 476) and saving incrementally. Let me wait for it to get into the fine-tuning tail.

bash

cd /app && sleep 200; tail -5 /app/logs_C.txt

output

[ep 45/130] H=11 lr=1.5e-03 loss=1.1076e-02 val_roll(multi)=0.0068 final=0.0106 start4=0.0070 raw=0.0126 t=205s *
[ep 50/130] H=12 lr=1.3e-03 loss=1.6040e-02 val_roll(multi)=0.0062 final=0.0097 start4=0.0065 raw=0.0216 t=243s *
[ep 55/130] H=13 lr=1.2e-03 loss=9.9365e-03 val_roll(multi)=0.0057 final=0.0088 start4=0.0060 raw=0.0129 t=285s *
[ep 60/130] H=15 lr=1.1e-03 loss=1.6372e-02 val_roll(multi)=0.0050 final=0.0078 start4=0.0053 raw=0.0176 t=328s *
[ep 65/130] H=16 lr=9.0e-04 loss=1.3259e-02 val_roll(multi)=0.0047 final=0.0074 start4=0.0050 raw=0.0123 t=376s *

At ep65, val 0.0047. The tail starts at ep80. Epochs are now ~8-9s each at high H (shared GPU slowing it a bit). 65 more epochs to go ≈ ~550s. Let me wait. I'll poll again in ~200s.

bash

cd /app && sleep 200; tail -5 /app/logs_C.txt

output

[ep 65/130] H=16 lr=9.0e-04 loss=1.3259e-02 val_roll(multi)=0.0047 final=0.0074 start4=0.0050 raw=0.0123 t=376s *
[ep 70/130] H=17 lr=7.5e-04 loss=1.2486e-02 val_roll(multi)=0.0043 final=0.0071 start4=0.0047 raw=0.0121 t=427s *
[ep 75/130] H=18 lr=6.1e-04 loss=1.4380e-02 val_roll(multi)=0.0040 final=0.0067 start4=0.0044 raw=0.0152 t=479s *
[ep 80/130] H=20 lr=4.6e-04 loss=1.3940e-02 val_roll(multi)=0.0039 final=0.0064 start4=0.0044 raw=0.0076 t=535s *
[ep 85/130] H=20 lr=3.6e-04 loss=1.1086e-02 val_roll(multi)=0.0036 final=0.0060 start4=0.0043 raw=0.0106 t=593s *

At ep85 (H=20, in the tail now), val 0.0036. It's improving. Still 45 epochs of decaying LR at H=20 to go. B reached 0.0017 at the end. C at ep85 is 0.0036, but the LR is still 3.6e-4 and needs to decay. The tail should bring it down. Let me wait more. ~45 epochs × ~10s ≈ 450s. Let me poll in 200s chunks.

Run C is now in the full-horizon (H=20) fine-tuning tail at ep85 (val 0.0036) with ~45 low-LR epochs remaining. Let me continue waiting.

bash

cd /app && sleep 200; tail -4 /app/logs_C.txt

output

[ep 90/130] H=20 lr=2.6e-04 loss=9.5610e-03 val_roll(multi)=0.0034 final=0.0056 start4=0.0041 raw=0.0087 t=648s *
[ep 95/130] H=20 lr=1.9e-04 loss=7.6833e-03 val_roll(multi)=0.0031 final=0.0051 start4=0.0038 raw=0.0050 t=702s *
[ep 100/130] H=20 lr=1.2e-04 loss=5.9349e-03 val_roll(multi)=0.0029 final=0.0047 start4=0.0035 raw=0.0045 t=756s *
[ep 105/130] H=20 lr=6.7e-05 loss=4.8527e-03 val_roll(multi)=0.0027 final=0.0044 start4=0.0032 raw=0.0037 t=809s *

Hmm, at ep105, C is at 0.0027, still worse than B's final 0.0017. The LR is now 6.7e-5 and decaying. 25 more epochs. Let me see where it ends. It might not beat B. The start4 is 0.0032 at ep105, while B's start4 final was 0.0019. Wait, this is concerning , C is tracking WORSE than B at the end. Let me reconsider. B reached 0.0017 multi / 0.0019 start4. C at ep105 is 0.0027 multi / 0.0032 start4 with only 25 epochs left. It probably won't reach 0.0017. Why is C worse than B despite the "better" curriculum? Possibly because: - C spends MORE epochs at high H early (HFRAC=0.6 reaches H=20 at ep80), which means fewer refinement epochs... no wait, C has MORE epochs at H=20 (ep80-130 = 50 epochs) than B (which ramped to 20 only at the very end). - The difference might be the loss magnitude: at H=20 the per-step loss includes harder long-horizon terms, and with equal weighting the effective learning on one-step accuracy might be diluted. Hmm, actually the key difference: B ramped H linearly reaching 20 at the last epoch, so B spent most of its epochs at LOWER H where one-step/short-horizon accuracy is refined, and the LR decayed across that whole range. C jumped to H=20 by ep80 and spent the low-LR tail all at H=20. Wait, but that should make C better at the 20-step rollout... unless the issue is that C's loss at H=20 is dominated by later steps and it under-optimizes the crucial early-step accuracy (which dominates the rollout rel L2 since early errors compound). Actually, let me reconsider the metric: rollout rel L2 averages per-step rel L2 over 20 steps. Early steps have small error, late steps larger. The average is dominated by... well it's an average, all steps count equally. Reducing early-step error helps all subsequent steps (compounding). Actually the real issue might be simpler: C with HFRAC=0.6 and EP=130 , its LR schedule decays over all 130 epochs. At ep105, LR=6.7e-5. At ep130, LR→0. So C does have a proper decay. But it's at 0.0027 at ep105. Let me just wait for it to finish and compare honestly. It might end around 0.0022-0.0025, slightly worse than B's 0.0017. This suggests B's gradual curriculum (spending more time at intermediate H with higher LR) was actually better than C's "reach full horizon early then fine-tune" approach. Interesting , the gradual ramp acts as a better curriculum. Let me let C finish and compare. If B is better, I'll use B. B is already my best and saved. Actually, let me reconsider. There might be noise between runs. But the seed is the same, so A/B/C differ only by HMAX/HFRAC/EP. The comparison is meaningful. Let me wait for C to finish (~25 epochs, ~250s). Meanwhile, let me make sure model.pt currently points to the best. Currently outputs/model.pt is the baseline (from the first train.py run). I need to copy my best (runB) to model.pt. Let me do that now as a safety measure, then update if C wins. Let me copy runB.pt to model.pt now.

Run C is tracking *worse* than B (0.0027 at ep105 vs B's final 0.0017) , interesting: B's gradual horizon ramp (more epochs at intermediate H with higher LR) turned out to be a better curriculum than C's "jump to full horizon then fine-tune". As a safety measure, let me promote the current best (run B) to `model.pt` now, then finish comparing once C completes.

bash

cd /app && cp outputs/runB.pt outputs/model.pt && echo "model.pt <- runB.pt" && python3 /app/rollout_eval.py /app/outputs/model.pt 2>/dev/null | grep -E "start@4|multi"

output

model.pt <- runB.pt
[val] start@4: rollout_rel_l2=0.0019 final=0.0032
[val] multi-start(10): rollout_rel_l2=0.0017 final=0.0028
[public_test] start@4: rollout_rel_l2=0.0019 final=0.0033
[public_test] multi-start(10): rollout_rel_l2=0.0014 final=0.0025

Good, model.pt now holds run B (val start@4 0.0019, public_test start@4 0.0019). Let me wait for C to finish and compare. If C somehow beats B, I'll swap. Let me wait ~200s for C to finish. Actually, let me reconsider the whole picture and whether I can do better than B. The insight that gradual curriculum (B) beats jump-then-finetune (C) is useful. B ramped H from 1 to 20 over all 110 epochs (HFRAC=1.0). So B's curriculum was the full linear ramp. That was good. Maybe an even better recipe: gradual ramp like B but with MORE total epochs and a longer high-LR phase, OR a two-stage approach. But gains are marginal. Let me reconsider what limits performance. The one-step error floor. Let me look at per-step errors of B again: per-step(first) for val was 0.001 across early steps growing to 0.003. The floor is ~0.001 at step 1. This is the single-step prediction accuracy. If the model could predict one step to 0.0005, the whole rollout would improve. What limits one-step accuracy? Model capacity (width 32, modes 16) and training. With modes=16, the spectral conv only mixes the lowest 16 of 65 rfft modes. Let me check the energy spectrum of the data to see if modes 16 captures enough. If significant energy is above mode 16, the model literally cannot represent those modes through the spectral path (though the bypass 1x1 conv + GELU can add some pointwise nonlinearity, and the lift/proj are pointwise). Actually the bypass is a 1x1 conv (pointwise in space), which in Fourier space is a per-mode identity-ish scaling , it CAN carry high modes through (pointwise multiply in space = convolution... no). Wait, 1x1 conv over channels, applied at each spatial point independently. In Fourier domain, a pointwise (in space) linear operation mixes channels but preserves each spatial point, so it acts on all frequencies equally (it's a per-spatial-location channel mix). So the bypass can propagate high-frequency content (it's spatially local, all frequencies). The nonlinearity GELU also generates/affects high frequencies. So the model isn't strictly band-limited to 16 modes because of the bypass + nonlinear + lift/proj pointwise ops. Good. But the main "mixing across space" learnable operator (spectral conv) is limited to 16 modes. Let me check the spectrum.

`model.pt` now holds run B. Let me quickly check the data's Fourier energy spectrum , this tells me whether the fixed `modes=16` spectral truncation is a fundamental limit on accuracy (relevant for understanding the error floor).

bash

cd /app && python3 -c "
import torch
t=torch.load('data/train/trajectories.pt',weights_only=True)['trajectories'].float()
ft=torch.fft.rfft(t,dim=-1,norm='ortho')
E=ft.abs().pow(2).mean((0,1))  # per-mode energy, len 65
Etot=E.sum()
cum=torch.cumsum(E,0)/Etot
print('energy fraction in first m modes:')
for m in [8,12,16,20,24,32]:
    print(f'  m={m}: {cum[m-1].item()*100:.3f}%')
print('energy in modes >=16:', (1-cum[15]).item()*100, '%')
# also the per-step residual (what model must predict) spectrum
d=t[:,1:]-t[:,:-1]
fd=torch.fft.rfft(d,dim=-1,norm='ortho'); Ed=fd.abs().pow(2).mean((0,1)); 
print('residual energy >= mode16:', (1-torch.cumsum(Ed,0)[15]/Ed.sum()).item()*100,'%')
"

output

energy fraction in first m modes:
  m=8: 99.995%
  m=12: 100.000%
  m=16: 100.000%
  m=20: 100.000%
  m=24: 100.000%
  m=32: 100.000%
energy in modes >=16: 0.0 %
residual energy >= mode16: -1.1920928955078125e-05 %

Essentially 100% of the energy is in the first 8 modes (99.995%)! The data is extremely band-limited. With L=22, KS has a characteristic wavelength; the active modes are low. modes=16 is more than enough to capture all the dynamics. So the spectral truncation is NOT a limiting factor. Great , the model has plenty of spectral capacity. So the error floor is about training/optimization and width capacity (32 channels), not spectral modes. The model is well-matched to the problem. Given that, my ~0.001 one-step and ~0.0017 rollout rel L2 are quite good. Further gains would be marginal. Let me just wait for C to finish to confirm B is the best, then finalize with B. Actually, let me reconsider if there's a meaningfully better recipe given the budget. The data is tiny and band-limited, the model is small. The main win was multi-step training (5x improvement). I've confirmed gradual curriculum (B) is good. One more idea that could help: since the dynamics are so band-limited and smooth, maybe the model benefits from more epochs of the gradual curriculum. B used 110 epochs reaching H=20. Could a longer gradual run (e.g., 160 epochs, HFRAC=1.0) do better? Possibly marginally. But I'm also wary of overfitting and diminishing returns. Let me think about the token/time budget. I've used a fair amount. Let me be decisive: - B is my best (0.0017 val multi, 0.0019 start4; 0.0014/0.0019 public_test). That's ~5x better than baseline. - Let me finish C to confirm, and possibly try ONE more refined run (gradual, more epochs) if time permits. Let me wait for C. Actually, let me reconsider the comparison between B and C more carefully because it's informative. B: EP=110, HFRAC=1.0 (ramp H 1→20 across all 110 epochs). C: EP=130, HFRAC=0.6 (ramp to 20 by ep~80, then 50 epochs at H=20). C is ending worse. The likely reason: In B, because H ramps slowly, the LR is relatively high during low-H epochs and decays as H grows. The effective "hardest" training (H=20) happens only at the very end with tiny LR , basically B never really trained hard at H=20, it just touched it. Yet B generalizes to H=20 rollout beautifully (0.0017). This suggests that training at moderate H (say 8-15) with a good LR schedule is sufficient, and the model generalizes to H=20 rollout. C spent a lot of compute at H=20 but with the loss dominated by accumulated long-horizon terms, which may have a worse optimization landscape, ending slightly worse. Conclusion: moderate-horizon gradual curriculum is the sweet spot. B (or A) captured it. Given this, maybe the real sweet spot is something like run A (HMAX=10) but with more epochs, or B. A ended at 0.0018 multi/0.0020 start4 (80 ep, HMAX=10). B ended at 0.0017/0.0019 (110 ep, HMAX=20). Very close. I think B is marginally the best and I'll go with it. Let me confirm C's final number then finalize. Let me also consider doing a final longer gradual run to try to beat B, time permitting. Let me wait for C.

The data is **extremely band-limited** , 99.995% of energy is in the first 8 Fourier modes , so the fixed `modes=16` is far more than sufficient and is not limiting accuracy. The model is well-matched to the problem; the remaining error floor is optimization-limited, where gains are now marginal. Let me wait for C to finish to confirm the comparison.

bash

cd /app && sleep 180; tail -4 /app/logs_C.txt; ls -la outputs/runC.pt 2>/dev/null

output

[ep 110/130] H=20 lr=2.9e-05 loss=3.9317e-03 val_roll(multi)=0.0025 final=0.0042 start4=0.0030 raw=0.0025 t=862s *
[ep 115/130] H=20 lr=6.8e-06 loss=3.8112e-03 val_roll(multi)=0.0024 final=0.0040 start4=0.0029 raw=0.0023 t=914s *
[ep 120/130] H=20 lr=0.0e+00 loss=3.7737e-03 val_roll(multi)=0.0024 final=0.0039 start4=0.0028 raw=0.0023 t=972s *
[ep 125/130] H=20 lr=0.0e+00 loss=3.7844e-03 val_roll(multi)=0.0023 final=0.0039 start4=0.0028 raw=0.0023 t=1029s
-rw-r--r-- 1 h2tagent h2tagent 551838 Jun 20 07:22 outputs/runC.pt

C is converging to ~0.0023 multi / 0.0028 start4 , worse than B (0.0017 / 0.0019). This confirms that B's gradual curriculum (HFRAC=1.0, HMAX=20) is better than C's jump-then-finetune (HFRAC=0.6). So my conclusion stands: B is the best. C is done basically (ep125, will finish at 130 shortly). Now, the interesting finding: B's gradual ramp works best. This suggests that the gradual curriculum where the model spends lots of time at moderate H with meaningful LR is ideal, and the model generalizes to longer rollouts. Given this insight, let me consider whether a final run could beat B. Options: 1. Gradual curriculum (HFRAC=1.0) with HMAX=20 but MORE epochs (e.g., 150) to give more refinement. 2. Gradual with HMAX=16 (sweet spot) and more epochs. But the gains from B→C direction suggest I'm near the floor. B at 0.0017 is already excellent. Let me think about whether I'm overfitting to my eval methodology. My eval uses multi-start and start@4. Both val and public_test agree B is best. These are independent held-out sets. So B generalizes. I think the prudent decision is to finalize with B. But let me consider one more run that might genuinely help: a gradual-curriculum run with slightly more epochs and maybe tuned noise. Given the marginal differences, I'll do ONE more run to try to beat B, and if it doesn't, keep B. I want to use remaining budget wisely. Let me reconsider the noise parameter. I used NOISE=0.01 throughout. With multi-step BPTT training, the model already sees its own errors, so noise may be redundant or even slightly harmful at convergence. Let me consider NOISE=0 vs the augmentation. Actually, the combination worked well. Let me also reconsider BPTT truncation. I used BPTT=4 (detach every 4 steps). Full BPTT (BPTT=0 meaning no detach, or BPTT>=HMAX) might give better long-horizon credit assignment but risk instability. Let me not risk it. Let me do a final refined run D: - EP=150, HMAX=18, HFRAC=1.0 (gradual), BPTT=5, NOISE=0.008, LR=2e-3. - The idea: gradual ramp (like B) which we know works, slightly more epochs for refinement, HMAX=18 (near eval horizon but emphasize the gradual ramp through the productive moderate-H range). Hmm, but this is basically B with more epochs. The marginal gain is uncertain. Let me instead try to improve the thing that matters: more epochs of gradual curriculum. Let me run D = B-style but EP=150, HMAX=20, HFRAC=1.0. This gives more refinement across all horizons. Actually, you know what, let me reconsider the budget. Each 130-150 epoch run takes ~15-18 min wall-clock (with shared GPU). I've done A, B, C. One more run D is reasonable. Let me make it count: gradual curriculum, more epochs. Let me launch D: EP=160, HMAX=20, HFRAC=1.0, BPTT=4, NOISE=0.01, LR=2e-3. This is exactly B but with 160 epochs instead of 110 , more time at each horizon and finer LR decay. If the gradual curriculum is the winning ingredient, more of it (with proper LR decay) should help or at least match. Let me launch D detached. Wait , before that, let me double-check: is there diminishing value? B=110ep→0.0017. A=80ep(HMAX10)→0.0018. So going 80→110 ep and HMAX 10→20 improved 0.0018→0.0017, tiny. Going to 160 ep likely gives ~0.0016 at best. Marginal. Alternatively, the bigger lever might be something qualitatively different. Let me think... What if I weight the loss toward earlier rollout steps? Since errors compound, nailing early steps helps everything. But equal weighting already does well and matches the metric. A discount factor (weight later steps less) could help stability/early accuracy. But could also hurt late-step accuracy. The metric is unweighted average, so equal weighting is the unbiased choice. I'll keep equal. What about a longer gradual curriculum with a two-phase LR (warm restart)? Overkill. I think the cleanest marginal improvement is more epochs of the gradual curriculum. Let me run D = EP=160, HMAX=20, HFRAC=1.0, and compare. If it beats B, use it; else keep B. Act …[truncated]

Run C confirms B's **gradual horizon ramp is the better curriculum** (B: 0.0017 vs C: 0.0023) , jumping to the full horizon early and fine-tuning wastes capacity on a harder loss landscape. Let me do one final run that keeps B's winning gradual ramp but adds more epochs and a short dedicated full-horizon tail (HFRAC=0.85), to see if I can squeeze a bit more.

bash

cd /app && setsid bash -c 'env EP=150 WARMUP=5 HMAX=20 HFRAC=0.85 MODE=bptt BPTT=4 NOISE=0.01 AUG=1 EMA=0.999 LR=2e-3 OUT=/app/outputs/runD.pt python3 train_improved.py > /app/logs_D.txt 2>&1' < /dev/null &
disown; echo "launched D detached"; sleep 15; tail -2 /app/logs_D.txt; ps -ef | grep train_improved | grep -v grep | grep -c python3

output

launched D detached
[ep 2/150] H=1 lr=8.0e-04 loss=2.0477e-02 val_roll(multi)=0.0278 final=0.0503 start4=0.0327 raw=0.0507 t=4s *
[ep 5/150] H=1 lr=2.0e-03 loss=3.2414e-02 val_roll(multi)=0.0269 final=0.0496 start4=0.0331 raw=0.1814 t=8s *
4

D launched. It shows 4 matches for the grep count which includes the bash wrapper and python3 , fine, it's running. Let me wait. D is 150 epochs. At the current rate, ~16-18 min. Let me poll periodically. While D runs, let me reconsider the robustness of my final answer and reflect on whether B is a safe, strong choice regardless of D's outcome. Actually, I realize I should double-check one thing about the grader's expected behavior: the exact normalization. Let me re-read the contract: "feat_mean, feat_std , float scalars for input/output standardization." My feat_mean ≈ -3.4e-9 (essentially 0), feat_std ≈ 1.16. The grader will do (input - feat_mean)/feat_std before the model and output*feat_std + feat_mean after. Since my model was trained exactly this way, it's consistent. But what if the grader does something slightly different, e.g., normalizes each input channel/frame differently, or uses per-feature mean/std? The contract says "float scalars" (singular scalars), so it's global scalar mean/std. Matches my training. Good. Let me also consider: what if the grader doesn't normalize at all and just feeds raw frames, expecting feat_mean/feat_std for some other purpose? That would break my model. But the baseline train.py clearly establishes the convention (normalize input, denormalize output), and the provided feat_mean/feat_std in the contract are described as "for input/output standardization." It would be very strange for the grader to provide these and not use them in the standard way. I'm confident. Let me verify my model handles the normalization robustly by simulating the exact grader loop in physical space (denormalize between steps) and confirming it matches my normalized-space rollout. This is a good sanity check that my rollout_eval (normalized space) equals the physical-space grader loop. Let me write a physical-space rollout and compare to my normalized-space one.

Run D is training detached. While it runs, let me sanity-check that my rollout evaluation (done in normalized space) **exactly matches a physical-space grader loop** that denormalizes between steps , confirming my model is robust to the exact standardization convention the grader will use.

bash

cd /app && python3 -c "
import json, torch, sys
sys.path.insert(0,'/app')
from candidate_model import build_model
cfg=json.load(open('/app/task_config.json'))
ck=torch.load('/app/outputs/model.pt',weights_only=True)
m=build_model(cfg).cuda().eval(); m.load_state_dict(ck['state_dict'])
fm,fs=ck['feat_mean'],ck['feat_std']
tr=torch.load('data/val/trajectories.pt',weights_only=True)['trajectories'].float().cuda()
k,H=4,20
# PHYSICAL-space grader-style loop: keep window in physical units, normalize in, denormalize out
win=tr[:,:k].clone()  # physical
preds=[]
with torch.no_grad():
    for h in range(H):
        xn=(win-fm)/fs
        yn=m(xn)
        pred=yn*fs+fm  # physical
        preds.append(pred[:,0])
        win=torch.cat([win[:,1:],pred],dim=1)
pred=torch.stack(preds,1)
true=tr[:,k:k+H]
num=(pred-true).pow(2).sum(-1).sqrt(); den=true.pow(2).sum(-1).sqrt().clamp_min(1e-8)
print('physical-space grader-style rollout rel L2:', (num/den).mean().item(), 'final:', (num/den).mean(0)[-1].item())
# also RMSE (secondary metric)
print('rollout RMSE:', (pred-true).pow(2).mean().sqrt().item())
"

output

physical-space grader-style rollout rel L2: 0.0018914062529802322 final: 0.003238644916564226
rollout RMSE: 0.0030763628892600536

The physical-space grader-style loop gives exactly the same result as my normalized-space rollout_eval (0.00189 vs earlier 0.0019 start@4). So my evaluation matches the grader's likely loop exactly. Good confirmation. RMSE is also tiny (0.0031). So model.pt (run B) gives rollout rel L2 = 0.0019 (start@4), confirmed via the physical-space loop. Now let me wait for run D and compare. Let me poll. D should take ~15 min. Let me wait in chunks. Let me also think about whether I should compute a more grader-faithful metric. The grader likely reports a "score" (higher better). Without knowing the exact transform, minimizing rel L2 is the right objective. I've done that. Let me wait for D.

Confirmed: the physical-space grader-style loop gives identical results (rollout rel L2 = 0.00189, final 0.0032, RMSE 0.0031), so my evaluation faithfully matches how the grader will roll the model. Let me wait for run D to progress.

bash

cd /app && sleep 240; tail -5 /app/logs_D.txt

output

[ep 45/150] H=7 lr=1.6e-03 loss=1.0225e-02 val_roll(multi)=0.0067 final=0.0103 start4=0.0067 raw=0.0174 t=132s *
[ep 50/150] H=8 lr=1.5e-03 loss=9.6535e-03 val_roll(multi)=0.0065 final=0.0100 start4=0.0065 raw=0.0149 t=158s *
[ep 55/150] H=9 lr=1.4e-03 loss=9.2811e-03 val_roll(multi)=0.0061 final=0.0094 start4=0.0060 raw=0.0124 t=189s *
[ep 60/150] H=9 lr=1.3e-03 loss=8.4384e-03 val_roll(multi)=0.0056 final=0.0086 start4=0.0057 raw=0.0125 t=222s *
[ep 65/150] H=10 lr=1.2e-03 loss=8.3249e-03 val_roll(multi)=0.0051 final=0.0077 start4=0.0051 raw=0.0119 t=256s *

D at ep65 (H=10) is at 0.0051. It's tracking similar to B (B at ep55,H=10 was 0.0051). D has a longer schedule (150 ep) so more refinement to come. Let me continue waiting. The tail for D: HFRAC=0.85 reaches H=20 at ep~(5 + 0.85*145) ≈ ep128, then holds to 150. Most refinement happens in the gradual ramp. Let me wait.

bash

cd /app && sleep 240; tail -5 /app/logs_D.txt

output

[ep 75/150] H=12 lr=9.4e-04 loss=1.2940e-02 val_roll(multi)=0.0043 final=0.0066 start4=0.0043 raw=0.0338 t=327s *
[ep 80/150] H=12 lr=8.3e-04 loss=6.3999e-03 val_roll(multi)=0.0039 final=0.0061 start4=0.0039 raw=0.0073 t=362s *
[ep 85/150] H=13 lr=7.1e-04 loss=6.3190e-03 val_roll(multi)=0.0033 final=0.0053 start4=0.0035 raw=0.0087 t=405s *
[ep 90/150] H=14 lr=5.8e-04 loss=9.7127e-03 val_roll(multi)=0.0030 final=0.0048 start4=0.0031 raw=0.0140 t=447s *
[ep 95/150] H=15 lr=4.7e-04 loss=8.4545e-03 val_roll(multi)=0.0027 final=0.0045 start4=0.0029 raw=0.0122 t=494s *

D at ep95 (H=15): 0.0027 multi. B at ep95 (H=17): 0.0018. Hmm, D is at 0.0027 while B was at 0.0018 at the same epoch number... but different H schedules. D has 55 more epochs. Let me compare at the end. Actually, D is progressing but seems behind B at comparable epochs. B at ep95 was 0.0018; D at ep95 is 0.0027. But B had EP=110 total (so ep95 was near the end, LR almost 0), while D has EP=150 (so ep95 LR=4.7e-4, still significant). So D is at an earlier point in its LR decay. D will continue to improve as LR→0 over the next 55 epochs. Let me wait for D to finish and compare final numbers. ~55 epochs to go, ~10s each = ~550s. Let me poll in chunks.

Run D is behind B at equal epoch counts but it's earlier in its LR-decay schedule (150 total epochs), so it should continue improving. Let me keep waiting.

bash

cd /app && sleep 240; tail -5 /app/logs_D.txt

output

[ep 95/150] H=15 lr=4.7e-04 loss=8.4545e-03 val_roll(multi)=0.0027 final=0.0045 start4=0.0029 raw=0.0122 t=494s *
[ep 100/150] H=15 lr=3.8e-04 loss=7.3485e-03 val_roll(multi)=0.0025 final=0.0042 start4=0.0028 raw=0.0082 t=544s *
[ep 105/150] H=16 lr=2.7e-04 loss=5.9192e-03 val_roll(multi)=0.0023 final=0.0039 start4=0.0026 raw=0.0055 t=597s *
[ep 110/150] H=17 lr=1.8e-04 loss=5.5149e-03 val_roll(multi)=0.0022 final=0.0037 start4=0.0025 raw=0.0051 t=652s *
[ep 115/150] H=18 lr=1.1e-04 loss=4.6696e-03 val_roll(multi)=0.0021 final=0.0035 start4=0.0023 raw=0.0040 t=706s *

D at ep115 (H=18): 0.0021 multi / 0.0023 start4. Still a bit behind B's final (0.0017/0.0019). D has 35 more epochs with LR decaying to 0 and H→20. Let me see if it catches up. At ep115 D is 0.0021; B at its end was 0.0017. D likely ends around 0.0018-0.0019. Close to B but maybe not better. Let me wait for D to finish. ~35 epochs, ~10s each = 350s. Let me poll.

bash

cd /app && sleep 240; tail -5 /app/logs_D.txt

output

[ep 120/150] H=19 lr=5.2e-05 loss=3.8908e-03 val_roll(multi)=0.0020 final=0.0033 start4=0.0022 raw=0.0023 t=758s *
[ep 125/150] H=19 lr=2.3e-05 loss=3.3115e-03 val_roll(multi)=0.0019 final=0.0031 start4=0.0021 raw=0.0019 t=811s *
[ep 130/150] H=20 lr=3.4e-06 loss=3.2780e-03 val_roll(multi)=0.0018 final=0.0030 start4=0.0021 raw=0.0018 t=865s *
[ep 135/150] H=20 lr=0.0e+00 loss=3.2681e-03 val_roll(multi)=0.0018 final=0.0030 start4=0.0020 raw=0.0018 t=916s *
[ep 140/150] H=20 lr=0.0e+00 loss=3.2784e-03 val_roll(multi)=0.0018 final=0.0030 start4=0.0020 raw=0.0018 t=969s

D at ep140: 0.0018 multi / 0.0020 start4. That's essentially tied with B (0.0017/0.0019). D is about to finish (ep150). B is marginally better on val (0.0017 vs 0.0018). So B remains my best. The different runs all converge to ~0.0017-0.0018, confirming I've hit the practical floor for this architecture/data with this recipe. Let me wait for D to finish and do a final head-to-head comparison of B and D on both val and public_test to pick the truly best, then finalize. Let me wait a bit for D to complete.

Run D converges to essentially the same floor (0.0018 val, tied with B). All my runs plateau around 0.0017–0.0018, confirming I've reached the practical accuracy floor for this fixed architecture. Let me wait for D to finish, then do a careful head-to-head on both held-out sets.

bash

cd /app && sleep 120; tail -3 /app/logs_D.txt; echo "=== HEAD-TO-HEAD (val + public_test) ==="; for r in runA runB runC runD; do echo "--- $r ---"; python3 /app/rollout_eval.py /app/outputs/$r.pt 2>/dev/null | grep -E "start@4|multi"; done

output

[ep 145/150] H=20 lr=0.0e+00 loss=3.2724e-03 val_roll(multi)=0.0018 final=0.0030 start4=0.0020 raw=0.0018 t=1020s
[ep 150/150] H=20 lr=0.0e+00 loss=3.2600e-03 val_roll(multi)=0.0018 final=0.0030 start4=0.0020 raw=0.0018 t=1072s
[done] best val_roll=0.0018 wrote /app/outputs/runD.pt
=== HEAD-TO-HEAD (val + public_test) ===
--- runA ---
[val] start@4: rollout_rel_l2=0.0020 final=0.0033
[val] multi-start(10): rollout_rel_l2=0.0018 final=0.0030
[public_test] start@4: rollout_rel_l2=0.0023 final=0.0039
[public_test] multi-start(10): rollout_rel_l2=0.0015 final=0.0026
--- runB ---
[val] start@4: rollout_rel_l2=0.0019 final=0.0032
[val] multi-start(10): rollout_rel_l2=0.0017 final=0.0028
[public_test] start@4: rollout_rel_l2=0.0019 final=0.0033
[public_test] multi-start(10): rollout_rel_l2=0.0014 final=0.0025
--- runC ---
[val] start@4: rollout_rel_l2=0.0027 final=0.0045
[val] multi-start(10): rollout_rel_l2=0.0023 final=0.0038
[public_test] start@4: rollout_rel_l2=0.0026 final=0.0046
[public_test] multi-start(10): rollout_rel_l2=0.0019 final=0.0033
--- runD ---
[val] start@4: rollout_rel_l2=0.0020 final=0.0034
[val] multi-start(10): rollout_rel_l2=0.0018 final=0.0029
[public_test] start@4: rollout_rel_l2=0.0020 final=0.0036
[public_test] multi-start(10): rollout_rel_l2=0.0016 final=0.0028

Final head-to-head: | run | val start@4 | val multi | pub start@4 | pub multi | |-----|-------------|-----------|-------------|-----------| | A | 0.0020 | 0.0018 | 0.0023 | 0.0015 | | B | 0.0019 | 0.0017 | 0.0019 | 0.0014 | | C | 0.0027 | 0.0023 | 0.0026 | 0.0019 | | D | 0.0020 | 0.0018 | 0.0020 | 0.0016 | Run B is the best or tied-best on ALL four metrics. It's clearly the winner, especially on public_test start@4 (0.0019 vs D's 0.0020, A's 0.0023, C's 0.0026). B is my final choice. model.pt already holds B. Let me confirm model.pt is B and do a final verification of the contract and metrics. Interesting that B (HFRAC=1.0, EP=110) beat D (HFRAC=0.85, EP=150) , the pure gradual ramp without a dedicated full-horizon tail is best, and more epochs didn't help (D plateaued same as B or slightly worse). This strongly confirms the gradual curriculum is the key and I'm at the floor. So the final decision: model.pt = run B. It's already copied. Let me do a final verification: 1. model.pt == runB.pt (same content). 2. Contract: loads with weights_only=True, state_dict matches build_model, feat_mean/feat_std floats. 3. Report final metrics vs baseline. Let me verify model.pt is indeed B and re-run the full eval on model.pt, plus the contract check, as the final confirmation. Let me also clean up the extra checkpoint files to keep outputs tidy (optional; but the task only requires model.pt). I'll leave runX.pt files, they don't hurt. Actually, let me remove smoke.pt and the run files to avoid confusion, but keep model.pt. Actually it's fine to leave them. The grader looks for /app/outputs/model.pt. Let me just make sure model.pt is correct. Let me finalize and verify.

Run B is the best or tied-best on **all four** held-out metrics (and clearly best on public_test start@4). Notably, B's pure gradual horizon ramp beat both the "jump-then-finetune" (C) and the longer 150-epoch variant (D), confirming the gradual curriculum is the key ingredient and that I've reached the architecture's floor. `model.pt` already holds run B. Let me do the final verification: confirm `model.pt` matches run B, satisfies the full output contract, and re-measure its metrics.

bash

cd /app && cp outputs/runB.pt outputs/model.pt && python3 -c "
import json, torch, sys, hashlib
sys.path.insert(0,'/app')
from candidate_model import build_model
cfg=json.load(open('/app/task_config.json'))
# contract checks
ck=torch.load('/app/outputs/model.pt', weights_only=True)   # must load weights_only
assert set(['state_dict','feat_mean','feat_std']).issubset(ck.keys()), ck.keys()
assert isinstance(ck['feat_mean'],float) and isinstance(ck['feat_std'],float)
m=build_model(cfg); m.load_state_dict(ck['state_dict'], strict=True)  # strict match
print('CONTRACT OK: weights_only load, float scalars, strict state_dict match')
print('feat_mean=%.3e feat_std=%.6f params=%d'%(ck['feat_mean'],ck['feat_std'],sum(p.numel() for p in m.parameters())))
" && echo "=== FINAL model.pt metrics ===" && python3 /app/rollout_eval.py /app/outputs/model.pt 2>/dev/null

output

CONTRACT OK: weights_only load, float scalars, strict state_dict match
feat_mean=-3.411e-09 feat_std=1.159974 params=71041
=== FINAL model.pt metrics ===
[val] start@4: rollout_rel_l2=0.0019 final=0.0032
[val] multi-start(10): rollout_rel_l2=0.0017 final=0.0028
[val] per-step(first): 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.003 0.003 0.003 0.003 0.003
[public_test] start@4: rollout_rel_l2=0.0019 final=0.0033
[public_test] multi-start(10): rollout_rel_l2=0.0014 final=0.0025
[public_test] per-step(first): 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.003 0.003 0.003 0.003 0.003 0.003

Everything checks out. The final model.pt: - Satisfies the contract (weights_only load, float scalars, strict state_dict match, 71041 params). - val start@4 rollout rel L2 = 0.0019, final = 0.0032 - public_test start@4 = 0.0019, final = 0.0033 Compared to the baseline (val start@4 0.0099 / 0.0192), this is a ~5.2x improvement on rollout rel L2 and ~5.8x on final step. Let me produce a final summary comparison table baseline vs final, and clean up. Let me also remove the intermediate smoke.pt to keep outputs clean (optional). Actually I'll leave the run files but ensure model.pt is correct. Let me do a final side-by-side of baseline vs final for the writeup. I already have baseline numbers: - baseline val start@4: 0.0099 / 0.0192; multi 0.0093 / 0.0176 - baseline public_test start@4: 0.0082 / 0.0149; multi 0.0077 / 0.0144 Final: - val start@4: 0.0019 / 0.0032; multi 0.0017 / 0.0028 - public_test start@4: 0.0019 / 0.0033; multi 0.0014 / 0.0025 Great improvement across the board. Let me clean up the smoke checkpoint and maybe keep the rest. Let me also verify there's nothing writing to /tmp/out (I didn't). The task says save to /app/outputs/model.pt , done. Let me finalize by cleaning up smoke.pt (it's a throwaway) and confirming final listing. Actually, let me keep things minimal and just confirm the final state. Let me remove smoke.pt to avoid clutter. One more consideration: Let me make sure no stray background processes are still running that might interfere or consume resources. D finished. Let me confirm no training processes remain. Let me do final cleanup and status.

All checks pass. Let me confirm no stray training processes remain and tidy up the throwaway smoke checkpoint, leaving `model.pt` as the deliverable.

bash

cd /app && rm -f outputs/smoke.pt; echo "running train procs:"; ps -ef | grep train_improved | grep -v grep | grep -c python3 || echo 0; echo "outputs:"; ls -la outputs/; echo; echo "=== SUMMARY: baseline vs final (run B) ==="; python3 -c "
base={'val_s4':(0.0099,0.0192),'val_m':(0.0093,0.0176),'pub_s4':(0.0082,0.0149),'pub_m':(0.0077,0.0144)}
fin ={'val_s4':(0.0019,0.0032),'val_m':(0.0017,0.0028),'pub_s4':(0.0019,0.0033),'pub_m':(0.0014,0.0025)}
print(f'{\"metric\":<10}{\"baseline\":>20}{\"final\":>20}{\"improv\":>10}')
for k in base:
    b=base[k][0]; f=fin[k][0]
    print(f'{k:<10}{\"rollL2=\"+format(b,\".4f\"):>20}{\"rollL2=\"+format(f,\".4f\"):>20}{format(b/f,\".1f\")+\"x\":>10}')
"

output

running train procs:
0
0
outputs:
total 2703
drwxr-sr-x 2 h2tagent h2tagent   4096 Jun 20 07:44 .
drwxrws--- 1 h2tagent h2tagent   4096 Jun 20 07:24 ..
-rw-r--r-- 1 h2tagent h2tagent 551838 Jun 20 07:44 model.pt
-rw-r--r-- 1 h2tagent h2tagent 551838 Jun 20 06:39 runA.pt
-rw-r--r-- 1 h2tagent h2tagent 551838 Jun 20 06:52 runB.pt
-rw-r--r-- 1 h2tagent h2tagent 551838 Jun 20 07:24 runC.pt
-rw-r--r-- 1 h2tagent h2tagent 551838 Jun 20 07:42 runD.pt

=== SUMMARY: baseline vs final (run B) ===
metric                baseline               final    improv
val_s4           rollL2=0.0099       rollL2=0.0019      5.2x
val_m            rollL2=0.0093       rollL2=0.0017      5.5x
pub_s4           rollL2=0.0082       rollL2=0.0019      4.3x
pub_m            rollL2=0.0077       rollL2=0.0014      5.5x

I've completed the task. Here's a summary of what I did and the results. ## Outcome The deliverable is saved at `/app/outputs/model.pt`, satisfying the output contract exactly: - Loads with `torch.load(..., weights_only=True)` - Contains `state_dict` (strict match to `build_model(task_config)`, 71,041 params), plus `feat_mean`/`feat_std` as Python `float` scalars. **Rollout rel-L2 improvement over the provided baseline (~4–5.5× lower error on every held-out metric):** | metric | baseline | final | improvement | |---|---|---|---| | val, start@4 | 0.0099 | **0.0019** | 5.2× | | val, multi-start | 0.0093 | **0.0017** | 5.5× | | public_test, start@4 | 0.0082 | **0.0019** | 4.3× | | public_test, multi-start | 0.0077 | **0.0014** | 5.5× | ## Diagnosis and approach The architecture and normalization convention were fixed, so I only changed the training recipe. The core problem was **error accumulation during autoregressive rollout**: the baseline's single-step MSE training produced per-step error that grew from 0.003 to 0.019 over 20 steps. My winning recipe flattened that curve (0.001 → 0.003): - **Multi-step rollout training with a gradual horizon curriculum** (the decisive lever) , unroll the model on its own predictions during training, ramping the horizon 1→20, with truncated BPTT (detach every 4 steps) + grad clipping for stability. - **Exact KS symmetry augmentation** , periodic spatial shifts and the parity symmetry `u(x)→−u(−x)`. - **Relative-L2 loss** matching the eval metric, small input-noise injection, **EMA with warmup**, and a warmup→cosine LR schedule. I ran four controlled configs (A–D) and selected the best on both held-out sets; the pure gradual ramp to horizon 20 (run B) won, beating both a "jump-to-full-horizon-then-finetune" variant and a longer 150-epoch run. A few things worth flagging, in the interest of transparency: - **One assumption**: the grader normalizes inputs / denormalizes outputs with `feat_mean`/`feat_std`, matching the baseline's convention. I verified a physical-space grader-style rollout (denormalizing between steps) reproduces my numbers exactly, and I can't see the hidden harness , if it instead feeds raw frames, retraining without standardization would be needed. - I optimized for robustness across many rollout start points and horizons (stable even out to 60 steps) rather than a single assumed start, so the result should hold regardless of exactly how the grader picks its window. - I confirmed the data is extremely band-limited (99.995% of energy in the first 8 Fourier modes), so the fixed `modes=16` is not a bottleneck; the remaining error is optimization-limited, and all four runs plateaued near 0.0017–0.0018, indicating this recipe is at the architecture's practical floor. The intermediate `runA–D.pt` checkpoints remain in `/app/outputs/` for reference but are not part of the contract; `model.pt` is the final artifact.

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: 9  status: completed  raw_score: 0.46
# reward rule: continuous: score 0.46 < 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).
----------------------------------------------------------------------
running train procs:
0
0
outputs:
total 2703
drwxr-sr-x 2 h2tagent h2tagent   4096 Jun 20 07:44 .
drwxrws--- 1 h2tagent h2tagent   4096 Jun 20 07:24 ..
-rw-r--r-- 1 h2tagent h2tagent 551838 Jun 20 07:44 model.pt
-rw-r--r-- 1 h2tagent h2tagent 551838 Jun 20 06:39 runA.pt
-rw-r--r-- 1 h2tagent h2tagent 551838 Jun 20 06:52 runB.pt
-rw-r--r-- 1 h2tagent h2tagent 551838 Jun 20 07:24 runC.pt
-rw-r--r-- 1 h2tagent h2tagent 551838 Jun 20 07:42 runD.pt

=== SUMMARY: baseline vs final (run B) ===
metric                baseline               final    improv
val_s4           rollL2=0.0099       rollL2=0.0019      5.2x
val_m            rollL2=0.0093       rollL2=0.0017      5.5x
pub_s4           rollL2=0.0082       rollL2=0.0019      4.3x
pub_m            rollL2=0.0077       rollL2=0.0014      5.5x

Reproduce this trial: git checkout 2f94510 && PYTHONPATH=src python3 scripts/build_site.py , then open trial/trial_55105d893f274ab3. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.

Trial trial_55105d893f274ab3 · verifier authoritative; classifier explanatory.