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Achieving up to 95% success in robotic manipulation tasks, this framework redefines the boundaries of sample efficiency and policy performance in real-world online reinforcement learning.
Forget hand-engineered reward functions: Reward-Zero uses language embeddings to give RL agents an intrinsic "sense of completion," dramatically improving sample efficiency and generalization.
