@inproceedings{wang-etal-2026-demystifying,
title = "Demystifying Hybrid Thinking: Can {LLM}s Truly Switch Between Think and No-Think?",
author = "Wang, Shouren and
Yang, Wang and
Long, Xianxuan and
Ganguly, Debargha and
Wang, Qifan and
Chaudhary, Vipin and
Han, Xiaotian",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.151/",
pages = "3491--3511",
ISBN = "979-8-89176-459-0",
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 ``wait'' (from 5917 to 522 on MATH500). Our findings highlight the limitations of current hybrid thinking and point the ways for enhancing its controllability."
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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 “wait” (from 5917 to 522 on MATH500). Our findings highlight the limitations of current hybrid thinking and point the ways for enhancing its controllability.</abstract>
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%0 Conference Proceedings
%T Demystifying Hybrid Thinking: Can LLMs Truly Switch Between Think and No-Think?
%A Wang, Shouren
%A Yang, Wang
%A Long, Xianxuan
%A Ganguly, Debargha
%A Wang, Qifan
%A Chaudhary, Vipin
%A Han, Xiaotian
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F wang-etal-2026-demystifying
%X 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 “wait” (from 5917 to 522 on MATH500). Our findings highlight the limitations of current hybrid thinking and point the ways for enhancing its controllability.
%U https://aclanthology.org/2026.emnlp-main.151/
%P 3491-3511
Markdown (Informal)
[Demystifying Hybrid Thinking: Can LLMs Truly Switch Between Think and No-Think?](https://aclanthology.org/2026.emnlp-main.151/) (Wang et al., EMNLP 2026)
ACL