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, MA TH-500, and AIME show that MixReasoning shortens reasoning length and substantially improves efficiency without compromising accuracy. Large Reasoning Models (LRMs) such as DeepSeek-R1 (Guo et al., 2025) and Qwen3 (Y ang et al., 2025a) have achieved state-of-the-art results on a wide range of complex tasks, spanning arithmetic, commonsense, and scientific reasoning. A key driver of these gains is the use of long chains of thought (CoTs) (Wei et al., 2022) that externalize intermediate computations before arriving at a final answer (Jaech et al., 2024).