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Forget uniform resampling – targeting high-entropy decision points in reasoning traces unlocks better performance than RL-trained models, all without extra training data or compute.
LLM judges inflate math proof scores by up to 0.36 points, revealing a significant alignment gap with human experts and a reasoning breakdown in discrete domains.
