Dynasor

TL;DR: We observe reasoning models often exhibit poor token efficiency: they waste many tokens second-guessing themselves. We develop Dynasor-CoT, a certainty-based approach for dynamically allocating inference compute for reasoning models. The intuition is that by probing reasoning models at intermediate steps, we can identify and early terminate problems where they maintain consistently high certainty in their answers. The method is plug-and-play, requiring no model modifications or training, but matches baseline accuracy on benchmarks like AMC23, AIME24, and MATH500 while reducing token consumption by 29% dataset-wide and up to 81% for single problems.

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