RedFlow: Redirect Failure into Action-Level Corrections for Flow-matching VLA Policy

arXiv:2607.27782v1 Announce Type: new Abstract: Flow-matching Vision-Language-Action (VLA) policies have shown strong potential for robotic manipulation but often suffer from compounding errors caused by distribution shifts during deployment. While offline reinforcement learning (RL) provides a practical way to improve deployed policies using rollout data, existing methods either ignore failure data or exploit it only at the trajectory level, resulting in low learning efficiency and persistent e...