@inproceedings{yang-etal-2026-mid,
title = "Mid-Think: Training-Free Intermediate-Budget Reasoning via Token-Level Triggers",
author = "Yang, Van and
Wang, Shouren and
Ganguly, Debargha and
Li, Xinpeng and
Song, Chaoda and
Singh, Vikash and
Chaudhary, Vipin and
Han, Xiaotian",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.299/",
pages = "6024--6038",
ISBN = "979-8-89176-395-1",
abstract = "Reasoning language models are controlled through explicit modes such as Think and No-think, yet we find that these behaviors are largely governed by a few token-level triggers rather than high-level instructions. Through attention analysis and controlled prompting experiments, we show that a leading ``Okay'' token induces reasoning behavior, while the newline pattern following `{\ensuremath{<}}/think{\ensuremath{>}}{`} suppresses it. Based on this observation, we propose Mid-Think, a simple training-free prompting format that combines these triggers to achieve intermediate-budget reasoning, consistently outperforming fixed-token and prompt-based baselines in terms of the accuracy{--}length trade-off. Furthermore, applying Mid-Think to RL training after SFT reduces training time by approximately 15{\%} while improving final performance of Qwen3-8B on AIME from 69.8{\%} to 72.4{\%} and on GPQA from 58.5{\%} to 61.1{\%}, demonstrating its effectiveness for both inference-time control and RL-based reasoning training."
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<abstract>Reasoning language models are controlled through explicit modes such as Think and No-think, yet we find that these behaviors are largely governed by a few token-level triggers rather than high-level instructions. Through attention analysis and controlled prompting experiments, we show that a leading “Okay” token induces reasoning behavior, while the newline pattern following ‘\ensuremath</think\ensuremath>‘ suppresses it. Based on this observation, we propose Mid-Think, a simple training-free prompting format that combines these triggers to achieve intermediate-budget reasoning, consistently outperforming fixed-token and prompt-based baselines in terms of the accuracy–length trade-off. Furthermore, applying Mid-Think to RL training after SFT reduces training time by approximately 15% while improving final performance of Qwen3-8B on AIME from 69.8% to 72.4% and on GPQA from 58.5% to 61.1%, demonstrating its effectiveness for both inference-time control and RL-based reasoning training.</abstract>
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%0 Conference Proceedings
%T Mid-Think: Training-Free Intermediate-Budget Reasoning via Token-Level Triggers
%A Yang, Van
%A Wang, Shouren
%A Ganguly, Debargha
%A Li, Xinpeng
%A Song, Chaoda
%A Singh, Vikash
%A Chaudhary, Vipin
%A Han, Xiaotian
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F yang-etal-2026-mid
%X Reasoning language models are controlled through explicit modes such as Think and No-think, yet we find that these behaviors are largely governed by a few token-level triggers rather than high-level instructions. Through attention analysis and controlled prompting experiments, we show that a leading “Okay” token induces reasoning behavior, while the newline pattern following ‘\ensuremath</think\ensuremath>‘ suppresses it. Based on this observation, we propose Mid-Think, a simple training-free prompting format that combines these triggers to achieve intermediate-budget reasoning, consistently outperforming fixed-token and prompt-based baselines in terms of the accuracy–length trade-off. Furthermore, applying Mid-Think to RL training after SFT reduces training time by approximately 15% while improving final performance of Qwen3-8B on AIME from 69.8% to 72.4% and on GPQA from 58.5% to 61.1%, demonstrating its effectiveness for both inference-time control and RL-based reasoning training.
%U https://aclanthology.org/2026.findings-acl.299/
%P 6024-6038
Markdown (Informal)
[Mid-Think: Training-Free Intermediate-Budget Reasoning via Token-Level Triggers](https://aclanthology.org/2026.findings-acl.299/) (Yang et al., Findings 2026)
ACL
- Van Yang, Shouren Wang, Debargha Ganguly, Xinpeng Li, Chaoda Song, Vikash Singh, Vipin Chaudhary, and Xiaotian Han. 2026. Mid-Think: Training-Free Intermediate-Budget Reasoning via Token-Level Triggers. In Findings of the Association for Computational Linguistics: ACL 2026, pages 6024–6038, San Diego, California, United States. Association for Computational Linguistics.