MixReasoning: Switching Modes to Think
Authors: name: Haiquan Lu, name: Gongfan Fang, name: Xinyin Ma, name: Qi Li, name: Xinchao Wang
Organization: National University of Singapore

Reasoning models enhance performance by tackling problems in a step-by-step
manner, decomposing them into sub-problems and exploring long chains of thought
before producing an answer. However, applying extended reasoning to every step
introduces substantial redundancy, as sub-problems vary widely in difficulty
and complexity: a small number of pivotal steps are genuinely challenging and
decisive for the final answer, while many others only involve straightforward
revisions or simple computations. Therefore, a natural idea is to endow
reasoning models with the ability to adaptively respond to this variation,
rather than treating all steps with the same level of elaboration. To this end,
we propose MixReasoning, a framework that dynamically adjusts the depth of
reasoning within a single response. The resulting chain of thought then becomes
a mixture of detailed reasoning on difficult steps and concise inference on
simpler ones. Experiments on GSM8K, MATH-500, and AIME show that MixReasoning
shortens reasoning length and substantially improves efficiency without
compromising accuracy.