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arXiv · 2510.00546

ThinkBrake: Efficient Reasoning via Log-Probability Margin Guided Decoding

Sangjun Song·Minjae Oh·Seungkyu Lee·Sungmin Jo·Yohan Jo
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Abstract

Large Reasoning Models (LRMs) allocate substantial inference-time compute to Chain-of-Thought (CoT) reasoning, improving performance on mathematics, scientific QA, and tool usage. However, this introduces overthinking: LRMs often reach a correct intermediate solution, continue reasoning, and overwrite it with an incorrect answer. We first demonstrate that oracle stopping--where we inject at every sentence boundary and select the best stopping point in hindsight--improves average accuracy by 8% while reducing thinking tokens by 72%, exposing substantial overthinking. Motivated by this finding, we propose ThinkBrake, which monitors the log-probability margin between the top continuation token and at sentence boundaries, stopping reasoning when this margin narrows. ThinkBrake requires no training and achieves favorable accuracy-efficiency trade-offs across math, scientific QA, and tool usage benchmarks, reducing thinking token usage by up to 30%. Furthermore, we provide theoretical analysis showing that ThinkBrake is equivalent to test-time realignment with a reward bonus for the token.

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Sangjun Song, Minjae Oh, Seungkyu Lee, Sungmin Jo, Yohan Jo. 2025-10-01. ThinkBrake: Efficient Reasoning via Log-Probability Margin Guided Decoding. https://arxiv.org/abs/2510.00546

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