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.
@article{arxiv.2510.12680,
title = {Demystifying Hybrid Thinking: Can LLMs Truly Switch Between Think and No-Think?},
author = {Shouren Wang and Wang Yang and Xianxuan Long and Qifan Wang and Vipin Chaudhary and Xiaotian Han},
journal= {arXiv preprint arXiv:2510.12680},
year = {2025}
}