title: Let LLMs Break Free from Overthinking via Self-Braking Tuning
summary: Large reasoning models (LRMs), such as OpenAI o1 and DeepSeek-R1, have
  significantly enhanced their reasoning capabilities by generating longer chains
  of thought, demonstrating outstanding performance across a variety of tasks.
  However, this performance gain comes at the cost of a substantial increase in
  redundant reasoning during the generation process, leading to high
  computational overhead and exacerbating the issue of overthinking. Although
  numerous existing approaches aim to address the problem of overthinking, they
  often rely on external interventions. In this paper, we propose a novel
  framework, Self-Braking Tuning (SBT), which tackles overthinking from the
  perspective of allowing the model to regulate its own reasoning process, thus
  eliminating the reliance on external control mechanisms. We construct a set of
  overthinking identification metrics based on standard answers and design a
  systematic method to detect redundant reasoning. This method accurately
  identifies unnecessary steps within the reasoning trajectory and generates
  training signals for learning self-regulation behaviors. Building on this
  foundation, we develop a complete strategy for constructing data with adaptive
  reasoning lengths and introduce an innovative braking prompt mechanism that
  enables the model to naturally learn when to terminate reasoning at an
  appropriate point. Experiments across mathematical benchmarks (AIME, AMC,
  MATH500, GSM8K) demonstrate that our method reduces token consumption by up to
  60% while maintaining comparable accuracy to unconstrained models.
paper_authors._id: 682f34b52b9fdc24ae9de371
paper_authors.name: Haoran Zhao
paper_authors.status: claimed_verified
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paper_authors.fullname: Haoran Zhao
paper_authors.type: user
paper_authors.user: XinC6
paper_authors._id: 682f34b52b9fdc24ae9de372
paper_authors.name: Yuchen Yan
paper_authors.status: claimed_verified
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paper_authors.fullname: Yuchen Yan
paper_authors.type: user
paper_authors.user: yanyc
paper_authors._id: 682f34b52b9fdc24ae9de373
paper_authors.name: Yongliang Shen
paper_authors.status: claimed_verified
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paper_authors.fullname: Yongliang Shen
paper_authors.type: user
paper_authors.user: tricktreat
paper_authors._id: 682f34b52b9fdc24ae9de374
paper_authors.name: Haolei Xu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-26T08:17:57.247Z
paper_authors._id: 6692aff88db712bad780f02a
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paper_authors.fullname: xhl
paper_authors.type: user
paper_authors.user: zjuxhl
paper_authors._id: 682f34b52b9fdc24ae9de375
paper_authors.name: Wenqi Zhang
paper_authors._id: 682f34b52b9fdc24ae9de376
paper_authors.name: Kaitao Song
paper_authors._id: 682f34b52b9fdc24ae9de377
paper_authors.name: Jian Shao
paper_authors._id: 682f34b52b9fdc24ae9de378
paper_authors.name: Weiming Lu
paper_authors._id: 682f34b52b9fdc24ae9de379
paper_authors.name: Jun Xiao
paper_authors._id: 682f34b52b9fdc24ae9de37a
paper_authors.name: Yueting Zhuang
paper_title: Let LLMs Break Free from Overthinking via Self-Braking Tuning
paper_summary: Large reasoning models (LRMs), such as OpenAI o1 and DeepSeek-R1, have
  significantly enhanced their reasoning capabilities by generating longer chains
  of thought, demonstrating outstanding performance across a variety of tasks.
  However, this performance gain comes at the cost of a substantial increase in
  redundant reasoning during the generation process, leading to high
  computational overhead and exacerbating the issue of overthinking. Although
  numerous existing approaches aim to address the problem of overthinking, they
  often rely on external interventions. In this paper, we propose a novel
  framework, Self-Braking Tuning (SBT), which tackles overthinking from the
  perspective of allowing the model to regulate its own reasoning process, thus
  eliminating the reliance on external control mechanisms. We construct a set of
  overthinking identification metrics based on standard answers and design a
  systematic method to detect redundant reasoning. This method accurately
  identifies unnecessary steps within the reasoning trajectory and generates
  training signals for learning self-regulation behaviors. Building on this
  foundation, we develop a complete strategy for constructing data with adaptive
  reasoning lengths and introduce an innovative braking prompt mechanism that
  enables the model to naturally learn when to terminate reasoning at an
  appropriate point. Experiments across mathematical benchmarks (AIME, AMC,
  MATH500, GSM8K) demonstrate that our method reduces token consumption by up to
  60% while maintaining comparable accuracy to unconstrained models.
paper_ai_summary: A novel Self-Braking Tuning framework reduces overthinking and unnecessary computational overhead in large reasoning models by enabling the model to self-regulate its reasoning process.
paper_ai_keywords: large reasoning models self-braking tuning overthinking reasoning capabilities mathematical benchmarks adaptive reasoning lengths braking prompt mechanism