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