Heterogeneous Diffusion Forcing World Model — OGBench cube-triple (3 cubes)

• 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-triple (3 cubes) — 46 state dims, absolute raw states and absolute actions, checkpoint-owned min-max normalizers.
• Data: cube-triple-play-v0 (official OGBench play, 3,000 trajectories x 1001 steps); 2,703,000 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)

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 3).
• metrics.jsonl — full training/validation log.
• source_commit.txt — exact training source revision.

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