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-tuned VLA remains frozen throughout critic learning and deployment. Physical-robot experiments show an increase in aggregate success from 60.0% to 82.5% across six nominal manipulation tasks. Under six altered-lighting and object-distractor conditions evaluated on three of these tasks, aggregate success improves from 34.2% to 49.2%. Ablations and rollout analyses support the importance of the learned guidance direction and the critic's visual and proprioceptive inputs. With approximately 2.735M trainable critic parameters alongside a 0.45B-parameter VLA, GAF enables task outcomes to inform action generation through a compact inference-time guidance module.

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