Preprint Open access
Many Ways to Succeed: Diversity-Driven RL Fine-Tuning for VLA Generalization
Reinforcement learning (RL) fine-tuning improves vision-language-action (VLA) policies through closed-loop experience, yet generalization beyond the fine-tuning distribution remains limited. Our analysis reveals a selective reshaping of exploration: RL contracts behavior globally, yet diversifies successful trajectorie …