World-VLA-Loop: Closed-Loop Learning of Video World Model and VLA Policy
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866914598588776448 |
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| author | Liu, Xiaokang Bai, Zechen Ci, Hai Ma, Kevin Yuchen Shou, Mike Zheng |
| author_facet | Liu, Xiaokang Bai, Zechen Ci, Hai Ma, Kevin Yuchen Shou, Mike Zheng |
| contents | Reinforcement learning (RL) can refine Vision-Language-Action (VLA) policies beyond behavior cloning, but real-world RL remains expensive due to extensive rollouts, resets, supervision, and safety risks. Action-conditioned video world models offer an option to train in virtual environments, yet they exhibit imprecise action following, particularly on subtle near-success failures. Besides, they lack native reward signals for RL. Computing rewards based on inaccurate visual predictions remain unreliable. We introduce World-VLA-Loop, structured around two foundational designs and a higher-level co-evolving paradigm. We first curate SANS, dedicatedly mixing successful and near-success trajectories to improve action-outcome alignment. Then, we train a state-aware video world model that jointly predicts future frames and binary rewards from diffusion latents. It couples reward estimation to the generator rather than a separate module, and in turn, benefits visual prediction. Since VLA behavior shifts during RL, a fixed simulator can misalign with the updated policy, World-VLA-Loop therefore closes the loop by using the refined world model for iterative VLA post-training while feeding rollouts from each improved policy back to augment and fine-tune the world model. Across simulation and real-robot experiments, World-VLA-Loop substantially improves VLA performance while reducing reliance on costly physical interaction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_06508 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | World-VLA-Loop: Closed-Loop Learning of Video World Model and VLA Policy Liu, Xiaokang Bai, Zechen Ci, Hai Ma, Kevin Yuchen Shou, Mike Zheng Robotics I.2.9 Reinforcement learning (RL) can refine Vision-Language-Action (VLA) policies beyond behavior cloning, but real-world RL remains expensive due to extensive rollouts, resets, supervision, and safety risks. Action-conditioned video world models offer an option to train in virtual environments, yet they exhibit imprecise action following, particularly on subtle near-success failures. Besides, they lack native reward signals for RL. Computing rewards based on inaccurate visual predictions remain unreliable. We introduce World-VLA-Loop, structured around two foundational designs and a higher-level co-evolving paradigm. We first curate SANS, dedicatedly mixing successful and near-success trajectories to improve action-outcome alignment. Then, we train a state-aware video world model that jointly predicts future frames and binary rewards from diffusion latents. It couples reward estimation to the generator rather than a separate module, and in turn, benefits visual prediction. Since VLA behavior shifts during RL, a fixed simulator can misalign with the updated policy, World-VLA-Loop therefore closes the loop by using the refined world model for iterative VLA post-training while feeding rollouts from each improved policy back to augment and fine-tune the world model. Across simulation and real-robot experiments, World-VLA-Loop substantially improves VLA performance while reducing reliance on costly physical interaction. |
| title | World-VLA-Loop: Closed-Loop Learning of Video World Model and VLA Policy |
| topic | Robotics I.2.9 |
| url | https://arxiv.org/abs/2602.06508 |