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arXiv · 2510.12680

Demystifying Hybrid Thinking: Can LLMs Truly Switch Between Think and No-Think?

Shouren Wang·Wang Yang·Xianxuan Long·Qifan Wang·Vipin Chaudhary·Xiaotian Han
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Abstract

Hybrid thinking enables LLMs to switch between reasoning and direct answering, offering a balance between efficiency and reasoning capability. Yet our experiments reveal that current hybrid thinking LLMs only achieve partial mode separation: reasoning behaviors often leak into the no-think mode. To understand and mitigate this, we analyze the factors influencing controllability and identify four that matter most: (1) larger data scale, (2) using think and no-think answers from different questions rather than the same question, (3) a moderate increase in no-think data number, and (4) a two-phase strategy that first trains reasoning ability and then applies hybrid think training. Building on these findings, we propose a practical recipe that, compared to standard training, can maintain accuracy in both modes while significantly reducing no-think output length (from $1085$ to $585$ on MATH500) and occurrences of reasoning-supportive tokens such as ``\texttt{wait}'' (from $5917$ to $522$ on MATH500). Our findings highlight the limitations of current hybrid thinking and offer directions for strengthening its controllability.

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Shouren Wang, Wang Yang, Xianxuan Long, Qifan Wang, Vipin Chaudhary, Xiaotian Han. 2025-10-14. Demystifying Hybrid Thinking: Can LLMs Truly Switch Between Think and No-Think?. https://arxiv.org/abs/2510.12680

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