Guided Action Flow: Value-Guided Sampling for Frozen Vision-Language-Action Policies

arXiv:2607.02092v4 Announce Type: replace-cross Abstract: Reinforcement learning can improve vision-language-action (VLA) policies beyond supervised fine-tuning, although this typically involves further updates to the policy parameters. For flow-matching policies, iterative action generation provides an additional opportunity to incorporate task information during inference. We introduce Guided Action Flow (GAF), which learns a compact, observation-conditioned action-value critic from robot task rollouts and applies its action gradient to steer reverse-time flow sampling. The supervised-fine-t

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