EXPO-FT: Sample-Efficient Reinforcement Learning Finetuning for Vision-Language-Action Models 事件

EXPO-FT: Sample-Efficient Reinforcement Learning Finetuning for Vision-Language-Action Models arXiv:2605.25477v1 Announce Type: cross Abstract: The ability to efficiently and reliably learn new tasks has been a foundational challenge in robotics. Vision-Language-Action (VLA) models have demonstrated strong generalization across diverse manipulation tasks, yet pretrained policies consistently fall short of the reliability required for real-world deployment. Reinforcement learning (RL) fine-tunin