让大模型自动切换快慢思维，又快又准地解题。
Adaptive Dual Reasoner: Large Reasoning Models Can Think Efficiently by Hybrid Reasoning
- 通过快思与慢思双模式动态切换应对不同难度任务
- 数学推理任务中性能提升6.1%，输出长度减少50%以上
- 适合追求推理效率与准确率平衡的研究者
尽管长推理模型（LRMs）在各类推理任务中表现优异，但常因过度思考导致计算开销和推理延迟上升。为此，我们提出自适应双推理器（ADR），支持快思考与慢思考两种模式。ADR根据上下文复杂度动态切换模式。训练分两阶段：(1) 初始阶段采用监督微调（SFT），通过专用管道构建混合推理数据集，赋予模型融合双模式的能力；(2) 强化学习阶段引入熵引导的混合策略优化（EHPO），采用熵引导的动态展开策略，在高熵单元分支，并使用难度感知惩罚平衡快慢推理。在多个挑战性数学推理基准上，ADR在先进方法中实现了性能与效率的最优平衡。具体而言，性能最高提升6.1%，推理输出长度减少49.5%至59.3%。
原文摘要 · Abstract (English)
Although Long Reasoning Models (LRMs) have achieved superior performance on various reasoning scenarios, they often suffer from increased computational costs and inference latency caused by overthinking. To address these limitations, we propose Adaptive Dual Reasoner, which supports two reasoning modes: fast thinking and slow thinking. ADR dynamically alternates between these modes based on the contextual complexity during reasoning. ADR is trained in two stages: (1) A cold-start stage using supervised fine-tuning (SFT) to equip the model with the ability to integrate both fast and slow reasoning modes, in which we construct a hybrid reasoning dataset through a dedicated pipeline to provide large-scale supervision. (2) A reinforcement learning stage for optimizing reasoning effort, where we introduce Entropy-guided Hybrid Policy Optimization EHPO, an RL training framework employing an entropy-guided dynamic rollout strategy for branching at high-entropy units and a difficulty-aware penalty to balance fast and slow reasoning. Across challenging mathematical reasoning benchmarks, ADR achieves an effective balance between reasoning performance and efficiency among state-of-the-art approaches. Specifically, ADR yields a performance gain of up to 6.1%, while reducing the reasoning output length by 49.5% to 59.3%.
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