Exhaustive-4 HDF world model for cube-single

README.md

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+---
+license: mit
+tags:
+  - world-model
+  - diffusion-forcing
+  - model-based-rl
+  - robotics
+library_name: pytorch
+---
+
+# Heterogeneous Diffusion Forcing World Model — OGBench cube-single (1 cube)
+
+State-based world model trained with the exhaustive-4 protocol: exactly four
+complete passes over every H=101 window (stride 1, episode-bounded) of the
+offline dataset, global batch 256 (4x H200 DDP, 64/rank).
+
+- **Architecture**: semantic-slot factorized DiT (~9.6M params) — one token per
+  state scalar, bidirectional within-frame attention + causal temporal
+  attention, adaLN-Zero conditioning on the previous action and a per-frame
+  diffusion noise level (Diffusion Forcing).
+- **Task**: OGBench cube-single (1 cube) — 28 state dims, absolute raw
+  states and absolute actions, checkpoint-owned min-max normalizers.
+- **Data**: cube-single-play-v0 (official OGBench play, 1,000 trajectories x 1001 steps); 901,000 training windows per epoch.
+- **Diffusion**: 1000 train steps, squaredcos_cap_v2, sample-prediction,
+  20 DDIM inference steps, full-sequence sampling schedule.
+- **Source**: `Robo_Continual_Learning` commit `cc88389e9c06b2a8f3dca103718cce248210d468`
+  (`algorithms/world_model/heterogeneous_diffusion_forcing.py`).
+
+## Validation (H=100 rollout, mean reference-normalized MSE)
+
+| epoch | step | val MSE |
+|---|---|---|
+| 1 | 3,520 | 0.10265 |
+| 2 | 7,040 | 0.00402 |
+| 3 | 10,560 | 0.00239 |
+| 4 | 14,080 | 0.00184 |
+
+## Files
+
+- `world_model_final.pt` — EMA weights after exactly 4 exhaustive epochs
+  (**the protocol artifact**).
+- `world_model_best.pt` — EMA weights at the best validation epoch
+  (epoch 4).
+- `metrics.jsonl` — full training/validation log.
+- `source_commit.txt` — exact training source revision.
+
+## Loading
+
+```python
+import torch
+payload = torch.load("world_model_final.pt", map_location="cpu", weights_only=False)
+# payload["model"] holds the HeterogeneousDiffusionForcingModel state dict;
+# payload["model_config"] / payload["schema"] rebuild the exact model.
+```

metrics.jsonl

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