ADaPT: Token-Level Decoupling for Efficient Large Reasoning Models

Tingyun Li; Zishang Jiang; Jinyi Han; Xinyi Wang; Sihang Jiang; Han Xia; Zhaoqian Dai; Shuguang Ma; Fei Yu; Jiaqing Liang; Yanghua Xiao

arXiv · 2606.19919

ADaPT: Token-Level Decoupling for Efficient Large Reasoning Models

Tingyun Li·Zishang Jiang·Jinyi Han·Xinyi Wang·Sihang Jiang·Han Xia·Zhaoqian Dai·Shuguang Ma·Fei Yu·Jiaqing Liang·Yanghua Xiao

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Abstract

Large reasoning models rely on long chain-of-thought to achieve strong performance, but applying such reasoning uniformly incurs high computational cost. Existing efficiency-oriented methods attempt to shorten or mix reasoning strategies, yet often degrade reasoning capability. We identify the root cause as sequence-level coupling between efficiency incentives and correctness optimization, which implicitly penalizes long but correct reasoning trajectories. To address this issue, we propose Adaptive Dual-Process Thinking (ADaPT), a token-level dual-process framework that explicitly decouples efficiency and correctness signals during training. ADaPT introduces a mode-selection token to control fast and slow reasoning, applying efficiency-related rewards exclusively to this token to avoid penalizing correct long reasoning while encouraging efficiency when appropriate. Moreover, ADaPT enables precise and continuous control over the efficiency-performance trade-off at inference time: by adjusting the generation probability of the mode-selection token, a single trained model can smoothly move along the efficiency-performance Pareto frontier. Extensive experiments demonstrate that ADaPT significantly reduces inference cost while maintaining strong reasoning performance across multiple benchmarks.

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Tingyun Li, Zishang Jiang, Jinyi Han, Xinyi Wang, Sihang Jiang, Han Xia, Zhaoqian Dai, Shuguang Ma, Fei Yu, Jiaqing Liang, Yanghua Xiao. 2026-06-18. ADaPT: Token-Level Decoupling for Efficient Large Reasoning Models. https://arxiv.org/abs/2606.19919

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