When to Continue Thinking: Adaptive Thinking Mode Switching for
  Efficient Reasoning
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.
682ec5f6b16c79c271c8559e
Xiaoyun Zhang
claimed_verified
2025-05-22T07:15:16.038Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/c-UeSyHfO7QchwNXW9mgu.png
ZhangXiaoyun
user
DadaCloud01
682ec5f6b16c79c271c8559f
Jingqing Ruan
claimed_verified
2025-05-22T07:15:09.457Z
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Jingqing Ruan
user
Amanda2023
682ec5f6b16c79c271c855a0
Xing Ma
claimed_verified
2025-05-22T07:15:12.313Z
663bc46ae14047f71026c2b3
https://cdn-avatars.huggingface.co/v1/production/uploads/663bc46ae14047f71026c2b3/lo4zFH0aIkFUQSNuzJW4I.jpeg
Maxing
user
Machine981
682ec5f6b16c79c271c855a1
Yawen Zhu
claimed_verified
2025-05-22T08:47:18.660Z
682ecaa39dd5ef944657bc16
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/MGiOAd4-_0HpBfmXWsNmY.png
ZHUYAWEN
user
Yaawennn
682ec5f6b16c79c271c855a2
Haodong Zhao
682ec5f6b16c79c271c855a3
Hao Li
682ec5f6b16c79c271c855a4
Jiansong Chen
682ec5f6b16c79c271c855a5
Ke Zeng
682ec5f6b16c79c271c855a6
Xunliang Cai
When to Continue Thinking: Adaptive Thinking Mode Switching for
  Efficient Reasoning
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.
ASRR framework optimizes reasoning efficiency in large models by suppressing redundant information processing without significantly impacting performance or safety.
Large reasoning models LRMs Long-Thinking modes No-Thinking modes Internal Self-Recovery Mechanism Adaptive Self-Recovery Reasoning accuracy-aware length reward regulation reasoning budget pass@1 harmless rates safety benchmarks efficiency adaptive reasoning safety