S3-CoT: Self-Sampled Succinct Reasoning Enables Efficient Chain-of-Thought LLMs

• URL: http://arxiv.org/abs/2602.01982v1
• Date: Mon, 02 Feb 2026 11:37:36 GMT
• Title: S3-CoT: Self-Sampled Succinct Reasoning Enables Efficient Chain-of-Thought LLMs
• Authors: Yanrui Du, Sendong Zhao, Yibo Gao, Danyang Zhao, Qika Lin, Ming Ma, Jiayun Li, Yi Jiang, Kai He, Qianyi Xu, Bing Qin, Mengling Feng,
• Abstract summary: Large language models equipped with chain-of-thought (CoT) achieve strong performance and offer a window into behavior.<n>Recent evidence suggests that improvements in CoT capabilities often come with redundant reasoning processes.<n>Our study presents a self-sampling framework based on activation steering for efficient CoT learning.
• Score: 48.80914119283909
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) equipped with chain-of-thought (CoT) achieve strong performance and offer a window into LLM behavior. However, recent evidence suggests that improvements in CoT capabilities often come with redundant reasoning processes, motivating a key question: Can LLMs acquire a fast-thinking mode analogous to human System 1 reasoning? To explore this, our study presents a self-sampling framework based on activation steering for efficient CoT learning. Our method can induce style-aligned and variable-length reasoning traces from target LLMs themselves without any teacher guidance, thereby alleviating a central bottleneck of SFT-based methods-the scarcity of high-quality supervision data. Using filtered data by gold answers, we perform SFT for efficient CoT learning with (i) a human-like dual-cognitive system, and (ii) a progressive compression curriculum. Furthermore, we explore a self-evolution regime in which SFT is driven solely by prediction-consistent data of variable-length variants, eliminating the need for gold answers. Extensive experiments on math benchmarks, together with cross-domain generalization tests in medicine, show that our method yields stable improvements for both general and R1-style LLMs. Our data and model checkpoints can be found at https://github.com/DYR1/S3-CoT.

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