PARL-VLA: Pruning-Aware On-Policy Reinforcement Learning for Vision-Language-Action Model

Token pruning has emerged as an essential technique for enabling efficient and resilient inference for foundation models, showing great potential in accelerating the Reinforcement Learning (RL) optimization of Vision–Language–Action (VLA) Models. However, our indepth analysis indicates that directly transferring prior pruning strategies to RL is non-trivial. Inference-time pruning introduces a train–test discrepancy, whereas dynamically adjusting the pruning configuration leads to inconsistent policy rollout and improvement. Therefore, we propose PARL-VLA, a pruning-aware RL framework that enforces explorationexploitation pruning consistency by recording the rollout pruning configuration and replaying it during policy updates, enabling end-to-end co-adaptation between token pruning and policy optimization. PARLVLA further trains across a spectrum of token budgets to learn a single policy that remains reliable under different information budgets at test time. On LIBERO and LIBERO-Plus, PARL-VLA discards 55% of visual tokens, speeds up rollout policy forward by about 1.3× and policy updates by about 1.7×, and improves LIBERO-Plus robustness by up to 6.6 points (3.2 on average) without sacrificing in-distribution success. On RoboTwin2.0, PARL-VLA achieves 46.9% real-robot success over 405 trials, improving over RLinf at 37.0%.