MixReasoning: Switching Modes to Think
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.
68e6066c975ac4c405ef2187
Haiquan Lu
claimed_verified
2025-10-08T08:00:55.284Z
689cb792f522165a63e55e4f
https://cdn-avatars.huggingface.co/v1/production/uploads/689cb792f522165a63e55e4f/LIQv_bkx7rqZLax8CAuyV.jpeg
Haiquan Lu
user
haiquanlu
68e6066c975ac4c405ef2188
Gongfan Fang
68e6066c975ac4c405ef2189
Xinyin Ma
68e6066c975ac4c405ef218a
Qi Li
68e6066c975ac4c405ef218b
Xinchao Wang
MixReasoning: Switching Modes to Think
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.
MixReasoning dynamically adjusts reasoning depth in models to improve efficiency without sacrificing accuracy.
reasoning models step-by-step sub-problems long chains of thought adaptive response dynamic adjustment depth of reasoning chain of thought detailed reasoning concise inference GSM8K MATH-500 AIME