# Diversity-Driven RL Fine-Tuning

The authors analyze how reinforcement learning fine-tuning (RFT) reshapes exploration in vision-language-action (VLA) policies, finding that RFT contracts the overall behavior distribution while making successful trajectories more diverse, easier to elicit, and broader in task-valid solution coverage; they then propose DRIVE, which converts temporally aligned similarity among grouped rollouts under matched task conditions into a success-conditioned intrinsic reward, improving out-of-domain success on LIBERO-Plus, ManiSkill3, and RoboTwin 2.0 and raising average out-of-domain success from 64.1% to 73.3% on a dual-arm AgileX PiPER-X platform.

The authors analyze how reinforcement learning fine-tuning (RFT) reshapes exploration in vision-language-action (VLA) policies, finding that RFT contracts the overall behavior distribution while making successful trajectories more diverse, easier to elicit, and broader in task-valid solution coverage; they then propose DRIVE, which converts temporally aligned similarity among grouped rollouts under matched task conditions into a success-conditioned intrinsic reward, improving out-of-domain success on LIBERO-Plus, ManiSkill3, and RoboTwin 2.0 and raising average out-of-domain success from 64.1% to 73.3% on a dual-arm AgileX PiPER-X platform.