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 (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.