WAM-Policy — Video Forward Flow-map Distillation (v2)

Flow-map–distilled video student models for WAM-Policy v2, distilled from a frozen LingBot-VA RoboTwin teacher (WAN 2.2 WanTransformer3DModel). The student learns a two-time average-velocity flow-map so it can sample video latents in 4 steps instead of the teacher's 25.

Contents

Folder	Student	Train steps	Code
video_ffm_90k/	90,000-step student	90k	new dual-head v2
video_ffm_50k/	50,000-step student	50k	original video-head FFM
video_ffm_5k/	5,000-step student	5k	new dual-head v2

Each folder contains:

• teacher_25step.mp4 — teacher sampled with 25 steps
• student_4step.mp4 — student sampled with 4 steps
• teacher_vs_student.mp4 — side-by-side comparison
• offline_video_stream_metadata.json — render metadata (8 latent chunks, fps 10)
• checkpoint.pt — full model checkpoint (frozen teacher backbone + trained flow-map adapters, ~12 GB)
• adapters_only.pt — all non-backbone weights (the trained flow-map conditioners + video/action heads; everything except the frozen base_model.blocks and condition_embedder), ~267 MB. Reattach onto the public teacher backbone to reconstruct the student.

Teacher

Distilled from robbyant/lingbot-va-posttrain-robotwin (WAN 2.2 video transformer + VAE), kept frozen. Only video_time_pair_adapter and video_head_adapter are trained.

Data

RoboTwin WAN 2.2 precomputed video latents (robbyant/robotwin-clean-and-aug-lerobot), an 82,414-sample latent manifest.

Notes

• checkpoint.pt bundles the frozen teacher backbone, hence ~12 GB; the trained part is only the two small adapter modules (adapters_only.pt).
• Trained and rendered on an AMD Instinct MI355X (ROCm 7.0, lingbot_attn_mode=torch).
• The point of the comparison: the 4-step student vs the 25-step teacher — flow-map distillation trades sampling steps for a single learned two-time transition.