FV-PMP: Accelerating Vision-Language-Action Models with Feedback-Verified Plan-Memory Propagation

Vision-Language-Action (VLA) models achieve strong performance in robot ma- nipulation, but frequent policy refreshes impose substantial inference costs during closed-loop execution. Under receding-horizon control, an action-chunk VLA predicts multiple future actions, while only a limited portion of the predicted sequence is executed before the next policy refresh. Executing longer prefixes reduces inference frequency, but the predicted actions may become less compatible with the evolving state as the underlying observation becomes stale. This raises a question: can unexecuted predictions support subsequent control without be- ing directly replayed? We propose FV-PMP, a Feedback-Verified Plan-Memory Propagation framework for efficient closed-loop VLA execution. Between visual policy refreshes, a lightweight propagator regenerates actions from current propri- oceptive feedback and recent execution history. To support deeper propagation, FV-PMP converts the unexecuted action suffix into Remaining-Plan Memory, re- taining compact motion and task-progress cues as conditioning information rather than executable commands. A Candidate-Conditioned Continuation Verifier esti- mates whether a propagated candidate remains suitable for continued propagation under the current execution context, while hard execution constraints further regu- late propagation. Together, they determine whether execution continues through lightweight propagation or restores current visual conditioning through corrective or full replanning. Both replanning modes generate a fresh action chunk and renew the plan context. Experiments on SimplerEnv using the CogACT backbone show that FV-PMP reduces VLA invocation frequency to 32.38% of full-policy execu- tion, achieving 3.27× average inference acceleration without sacrificing aggregate closed-loop task performance