Computer Science > Computation and Language
arXiv:2603.08999 (cs)
[Submitted on 9 Mar 2026]
Title:Learning When to Sample: Confidence-Aware Self-Consistency for Efficient LLM Chain-of-Thought Reasoning
Authors:Juming Xiong, Kevin Guo, Congning Ni, Chao Yan, Katherine Brown, Avinash Baidya, Xiang Gao, Bradley Marlin, Zhijun Yin
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Abstract:Large language models (LLMs) achieve strong reasoning performance through chain-of-thought (CoT) reasoning, yet often generate unnecessarily long reasoning paths that incur high inference cost. Recent self-consistency-based approaches further improve accuracy but require sampling and aggregating multiple reasoning trajectories, leading to substantial additional computational overhead. This paper introduces a confidence-aware decision framework that analyzes a single completed reasoning trajectory to adaptively select between single-path and multi-path reasoning. The framework is trained using sentence-level numeric and linguistic features extracted from intermediate reasoning states in the MedQA dataset and generalizes effectively to MathQA, MedMCQA, and MMLU without additional fine-tuning. Experimental results show that the proposed method maintains accuracy comparable to multi-path baselines while using up to 80\% fewer tokens. These findings demonstrate that reasoning trajectories contain rich signals for uncertainty estimation, enabling a simple, transferable mechanism to balance accuracy and efficiency in LLM reasoning.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2603.08999 [cs.CL] |
| (or arXiv:2603.08999v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2603.08999
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View a PDF of the paper titled Learning When to Sample: Confidence-Aware Self-Consistency for Efficient LLM Chain-of-Thought Reasoning, by Juming Xiong and 8 other authors
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