Let LLMs Break Free from Overthinking via Self-Braking Tuning
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.
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Haoran Zhao
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2025-05-26T08:17:35.353Z
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Haoran Zhao
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XinC6
682f34b52b9fdc24ae9de372
Yuchen Yan
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2025-05-26T08:17:33.082Z
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Yuchen Yan
user
yanyc
682f34b52b9fdc24ae9de373
Yongliang Shen
claimed_verified
2025-05-26T08:17:30.588Z
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Yongliang Shen
user
tricktreat
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Haolei Xu
claimed_verified
2025-05-26T08:17:57.247Z
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xhl
user
zjuxhl
682f34b52b9fdc24ae9de375
Wenqi Zhang
682f34b52b9fdc24ae9de376
Kaitao Song
682f34b52b9fdc24ae9de377
Jian Shao
682f34b52b9fdc24ae9de378
Weiming Lu
682f34b52b9fdc24ae9de379
Jun Xiao
682f34b52b9fdc24ae9de37a
Yueting Zhuang
Let LLMs Break Free from Overthinking via Self-Braking Tuning
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.
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.
large reasoning models self-braking tuning overthinking reasoning capabilities mathematical benchmarks adaptive reasoning lengths braking prompt mechanism