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

- Source: arXiv cs.RO
- Published: 2026-10-28T04:00:00.000Z
- RoboHot: https://robohot.news/en/items/oqnnbzegbm41jukmxoxbbz6wc
- Original: https://arxiv.org/abs/2605.09410

## Summary

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.