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This paper proposes E³RL, a reinforcement learning method that uses dynamic epistemic entropy thresholds to enable LLMs to excise local logical defects during generation, overcoming the autoregressive curse in long-horizon reasoning and achieving state-of-the-art results on mathematical reasoning benchmarks like AIME.