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FLaRe is a framework that leverages large-scale reinforcement learning to fine-tune pre-trained robotic behavior cloning policies, enabling them to achieve adaptive and masterful performance. The framework improves success rates on mobile manipulation tasks by over 23% in simulation and 30% on real robots, demonstrating generalization to novel tasks and rapid adaptation to new robot embodiments using sparse rewards.