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Find Something You Can't Do: Agentic Real-World Reinforcement Learning for Self-Improving VLA Models
Vision--language--action (VLA) models provide strong priors for robotic manipulation but are typically deployed as frozen policies, unable to improve from their own failures. Real-world reinforcement learning (RL) offers a path to continued improvement, yet manual environment resets and task-success
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