RL post-training of vision-language-action models concentrates parameter updates in Timestep Modules that hold only 14.83%–27.58% of the action expert, and their low-rank directions predict and improve task success
This work systematically analyzes how reinforcement learning (RL) reshapes flow-based vision-language-action (VLA) models, finding that across π0.5 and GR00T N1.5/N1.6 on LIBERO, ManiSkill, MetaWorld, and CALVIN, RL induces low-rank parameter updates highly concentrated in the action expert's Timestep Modules, which hold 14.83%–27.58% of the parameters yet capture a disproportionate share of the RL gain, with shift-vector update directions predicting task success at up to 99.6% ROC-AUC and steering along them improving policies without additional RL training.