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Aligning utility training with policy optimization, PAWS overcomes distribution shifts that cripple traditional preference-based reinforcement learning methods.
Action chunking, previously limited to offline RL, can now achieve state-of-the-art performance in online settings thanks to SEAR's receding horizon approach.
