WAM-OPD: Joint Video-Action Supervision for World Action Model Post-Training with On-Policy Distillation
World Action Models (WAMs) generate both future video and robot actions, offering two connected outputs for post-training supervision. How can a pretrained WAM learn from a stronger Teacher on the histories it encounters during execution? We present WAM-OPD, which collects Student rollout histories and queries a Teacher for paired video and action targets. The Student learns from both targets while retaining its one-step video and action generation at deployment. Across 12 RoboTwin 2.0 tasks, WAM-OPD improves average success from 33.8% to 65.7%; across four real-robot tasks, it improves average success from 51.4% to 64.6%. With the collected Student histories held fixed, joint video-actio...
| Authors | Liuhaichen Yang, Zhengyang Zhong, Hanshang Zhu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 23 August 2026 |
| Last updated at source | 1 October 2026 |
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