PolicyTrim: Boosting Intrinsic Policy Efficiency of Vision-Language-Action Models

Vision-Language-Action (VLA) models provide a unifiedparadigm for robotic manipulation, yet their real-world deploymentremains limited by execution efficiency. While existing efforts predomi-nantly focus on compute-centric efficiency to reduce per-step inferencelatency, the intrinsic policy efficiency of these models remains largelyunexplored. Policy efficiency is fundamentally affected by two factors,namely the effective executable length of predicted action chunks and thetotal physical steps required to complete a task. These two factors jointlydetermine the number of forward inference calls during execution. Weobserve that current VLA policies struggle with planning unreliabilityand action redundancy, suffering from severe tail degradation in actionchunks and tending to generate redundant physical steps. To address this,we propose PolicyTrim, a reinforcement learning-based post-trainingframework that extends reliable action chunk length and reduces redun-dant physical steps. For reliable chunk extension, we employ a dynamicexploration strategy that rewards the successful completion of longer exe-cutable lengths, progressively pushing the trustworthy prediction horizonto its empirical limit. For step efficiency, we design a redundancy-awarereward that favors successful task completion with fewer steps whilepenalizing unreproducible shortcuts, effectively eliminating redundantphysical actions. Experiments on three benchmarks and three VLA mod-els show that PolicyTrim improves action chunk utilization by 3×, re-duces physical execution steps by 51.4%, and delivers up to a 5.83× end-to-end deployment speedup without compromising task success rates.Project Page: https://inceptionwang.github.io/PolicyTrim/