Think Just Enough: Sequence-Level Entropy as a Confidence Signal for LLM Reasoning

• URL: http://arxiv.org/abs/2510.08146v3
• Date: Tue, 28 Oct 2025 10:58:14 GMT
• Title: Think Just Enough: Sequence-Level Entropy as a Confidence Signal for LLM Reasoning
• Authors: Aman Sharma, Paras Chopra,
• Abstract summary: We introduce a novel entropy-based framework to drive token efficiency in large language models during reasoning tasks.<n>Our approach uses Shannon entropy from token-level logprobs as a confidence signal to enable early stopping.<n>We show that entropy-based confidence calibration represents an emergent property of advanced post-training optimization.
• Score: 5.37133760455631
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a simple, yet novel entropy-based framework to drive token efficiency in large language models during reasoning tasks. Our approach uses Shannon entropy from token-level logprobs as a confidence signal to enable early stopping, achieving 25-50% computational savings while maintaining task accuracy. Crucially, we demonstrate that entropy-based confidence calibration represents an emergent property of advanced post-training optimization present in modern reasoning models but notably absent in standard instruction-tuned and pre-trained models (Llama 3.3 70B). We show that the entropy threshold to stop reasoning varies from model to model but can be calculated easily in one shot using only a few examples from existing reasoning datasets. Our results indicate that advanced reasoning models often know that they've gotten a correct answer early on, and that this emergent confidence awareness can be exploited to save tokens and reduce latency. The framework demonstrates consistent performance across reasoning-optimized model families with 25-50% computational cost reduction while preserving accuracy, revealing that confidence mechanisms represent a distinguishing characteristic of modern post-trained reasoning systems versus their predecessors.

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