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RePO-VLA: Recovery-Driven Policy Optimization for Vision-Language-Action Models

RePO-VLA proposes a recovery-driven policy optimization framework that assigns distinct roles to success, recovery, and failure trajectories for long-horizon, contact-rich manipulation tasks. The method first uses recovery-aware initialization to slice corrective segments into new episodes, then learns a progress-aware semantic value function, and uses value-conditioned refinement to make the policy prefer high-progress actions.

Part of the event: RePO-VLA Releases Recovery-Driven Policy Optimization Framework

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