PolicyTrim Targets Action Redundancy in VLA Robotic Models

PolicyTrim addresses two underexplored efficiency bottlenecks in Vision-Language-Action models: effective executable action chunk length and total physical steps per task, which together determine total inference calls during robot execution. Current VLA policies suffer from prediction degradation at the tail of action chunks and action redundancy, distinct from per-step compute latency.