title: When to Continue Thinking: Adaptive Thinking Mode Switching for
    Efficient Reasoning
summary: 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.
paper_authors._id: 682ec5f6b16c79c271c8559e
paper_authors.name: Xiaoyun Zhang
paper_authors.status: claimed_verified
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paper_authors.fullname: ZhangXiaoyun
paper_authors.type: user
paper_authors.user: DadaCloud01
paper_authors._id: 682ec5f6b16c79c271c8559f
paper_authors.name: Jingqing Ruan
paper_authors.status: claimed_verified
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paper_authors.avatarUrl: /avatars/9743b5a2b6f9b9990aa1cb06bd6eb5c6.svg
paper_authors.fullname: Jingqing Ruan
paper_authors.type: user
paper_authors.user: Amanda2023
paper_authors._id: 682ec5f6b16c79c271c855a0
paper_authors.name: Xing Ma
paper_authors.status: claimed_verified
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paper_authors.fullname: Maxing
paper_authors.type: user
paper_authors.user: Machine981
paper_authors._id: 682ec5f6b16c79c271c855a1
paper_authors.name: Yawen Zhu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-22T08:47:18.660Z
paper_authors._id: 682ecaa39dd5ef944657bc16
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paper_authors.fullname: ZHUYAWEN
paper_authors.type: user
paper_authors.user: Yaawennn
paper_authors._id: 682ec5f6b16c79c271c855a2
paper_authors.name: Haodong Zhao
paper_authors._id: 682ec5f6b16c79c271c855a3
paper_authors.name: Hao Li
paper_authors._id: 682ec5f6b16c79c271c855a4
paper_authors.name: Jiansong Chen
paper_authors._id: 682ec5f6b16c79c271c855a5
paper_authors.name: Ke Zeng
paper_authors._id: 682ec5f6b16c79c271c855a6
paper_authors.name: Xunliang Cai
paper_title: When to Continue Thinking: Adaptive Thinking Mode Switching for
    Efficient Reasoning
paper_summary: 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.
paper_ai_summary: ASRR framework optimizes reasoning efficiency in large models by suppressing redundant information processing without significantly impacting performance or safety.
paper_ai_keywords: 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