@inproceedings{zhang-etal-2025-continue,
title = "When to Continue Thinking: Adaptive Thinking Mode Switching for Efficient Reasoning",
author = "Zhang, Xiaoyun and
Ruan, Jingqing and
Ma, Xing and
Zhu, Yawen and
Zhao, Haodong and
Li, Hao and
Chen, Jiansong and
Zeng, Ke and
Cai, Xunliang",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.310/",
pages = "5808--5828",
ISBN = "979-8-89176-335-7",
abstract = "Large reasoning models (LRMs) achieve remarkable performance via long reasoning chains, but often incur excessive computational overhead due to redundant reasoning, especially on simple tasks. In this work, we systematically quantify the upper bounds of LRMs under both Long-Thinking and No-Thinking modes, and uncover the phenomenon of ``Internal Self-Recovery Mechanism'' where models implicitly supplement reasoning during answer generation. Building on this insight, we propose Adaptive Self-Recovery Reasoning (ASRR), a framework that suppresses unnecessary reasoning and enables implicit recovery. By introducing accuracy-aware length reward regulation, ASRR adaptively allocates reasoning effort according to problem difficulty, achieving high efficiency with negligible performance sacrifice. Experiments across multiple benchmarks and models show that, compared with GRPO, ASRR reduces reasoning budget by up to 32.5{\%} (1.5B) and 25.7{\%} (7B) with minimal accuracy loss (1.2{\%} and 0.6{\%} pass@1), and significantly boosts harmless rates on safety benchmarks (up to +21.7{\%}). Our results highlight the potential of ASRR for enabling efficient, adaptive, and safer reasoning in LRMs."
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<abstract>Large reasoning models (LRMs) achieve remarkable performance via long reasoning chains, but often incur excessive computational overhead due to redundant reasoning, especially on simple tasks. In this work, we systematically quantify the upper bounds of LRMs under both Long-Thinking and No-Thinking modes, and uncover the phenomenon of “Internal Self-Recovery Mechanism” where models implicitly supplement reasoning during answer generation. Building on this insight, we propose Adaptive Self-Recovery Reasoning (ASRR), a framework that suppresses unnecessary reasoning and enables implicit recovery. By introducing accuracy-aware length reward regulation, ASRR adaptively allocates reasoning effort according to problem difficulty, achieving high efficiency with negligible performance sacrifice. Experiments across multiple benchmarks and models show that, compared with GRPO, ASRR reduces reasoning budget by up to 32.5% (1.5B) and 25.7% (7B) with minimal accuracy loss (1.2% and 0.6% pass@1), and significantly boosts harmless rates on safety benchmarks (up to +21.7%). Our results highlight the potential of ASRR for enabling efficient, adaptive, and safer reasoning in LRMs.</abstract>
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%0 Conference Proceedings
%T When to Continue Thinking: Adaptive Thinking Mode Switching for Efficient Reasoning
%A Zhang, Xiaoyun
%A Ruan, Jingqing
%A Ma, Xing
%A Zhu, Yawen
%A Zhao, Haodong
%A Li, Hao
%A Chen, Jiansong
%A Zeng, Ke
%A Cai, Xunliang
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F zhang-etal-2025-continue
%X Large reasoning models (LRMs) achieve remarkable performance via long reasoning chains, but often incur excessive computational overhead due to redundant reasoning, especially on simple tasks. In this work, we systematically quantify the upper bounds of LRMs under both Long-Thinking and No-Thinking modes, and uncover the phenomenon of “Internal Self-Recovery Mechanism” where models implicitly supplement reasoning during answer generation. Building on this insight, we propose Adaptive Self-Recovery Reasoning (ASRR), a framework that suppresses unnecessary reasoning and enables implicit recovery. By introducing accuracy-aware length reward regulation, ASRR adaptively allocates reasoning effort according to problem difficulty, achieving high efficiency with negligible performance sacrifice. Experiments across multiple benchmarks and models show that, compared with GRPO, ASRR reduces reasoning budget by up to 32.5% (1.5B) and 25.7% (7B) with minimal accuracy loss (1.2% and 0.6% pass@1), and significantly boosts harmless rates on safety benchmarks (up to +21.7%). Our results highlight the potential of ASRR for enabling efficient, adaptive, and safer reasoning in LRMs.
%U https://aclanthology.org/2025.findings-emnlp.310/
%P 5808-5828
Markdown (Informal)
[When to Continue Thinking: Adaptive Thinking Mode Switching for Efficient Reasoning](https://aclanthology.org/2025.findings-emnlp.310/) (Zhang et al., Findings 2025)
ACL
- Xiaoyun Zhang, Jingqing Ruan, Xing Ma, Yawen Zhu, Haodong Zhao, Hao Li, Jiansong Chen, Ke Zeng, and Xunliang Cai. 2025. When to Continue Thinking: Adaptive Thinking Mode Switching for Efficient Reasoning. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 5808–5828, Suzhou, China. Association for Computational Linguistics.