[Paper] Early Stopping for Large Reasoning Models via Confidence Dynamics

Summary

This paper addresses the challenge of computational cost and performance degradation in large reasoning models due to extended chain-of-thought generation. It proposes an "Early Stopping" mechanism by studying the confidence dynamics of intermediate answers during reasoning. The research observes that correct reasoning trajectories typically reach high-confidence answers, suggesting a method to determine optimal stopping points for these models.

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