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J-Miner reveals that decision knowledge from language models can be distilled into executable rules, achieving near-perfect fidelity while enhancing interpretability and reusability.
LLM agents can explore more effectively by retrieving and reasoning over off-policy step-level traces, leading to significant performance gains and faster training.
