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RynnValue shows that using temporal distance as a supervision target can outperform traditional preference-based methods in robotic learning, achieving higher accuracy and broader generalization.
Transition supervision can dramatically boost LLM agent performance, outperforming standard policy optimization by leveraging environmental feedback.
