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arXiv · 2506.00189

Control-R: Towards controllable test-time scaling

Di Zhang·Weida Wang·Junxian Li·Xunzhi Wang·Jiatong Li·Jianbo Wu·Jingdi Lei·Haonan He·Peng Ye·Shufei Zhang·Wanli Ouyang·Yuqiang Li·Dongzhan Zhou
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Abstract

This paper target in addressing the challenges of underthinking and overthinking in long chain-of-thought (CoT) reasoning for Large Reasoning Models (LRMs) by introducing Reasoning Control Fields (RCF)--a novel test-time approach that injects structured control signals to guide reasoning from a tree search perspective. RCF enables models to adjust reasoning effort according to given control conditions when solving complex tasks. Additionally, we present the Control-R-4K dataset, which consists of challenging problems annotated with detailed reasoning processes and corresponding control fields. To further enhance reasoning control, we propose a Conditional Distillation Finetuning (CDF) method, which trains model--particularly Control-R-32B--to effectively adjust reasoning effort during test time. Experimental results on benchmarks such as AIME2024 and MATH500 demonstrate that our approach achieves state-of-the-art performance at the 32B scale while enabling a controllable Long CoT reasoning process (L-CoT). Overall, this work introduces an effective paradigm for controllable test-time scaling reasoning.

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Di Zhang, Weida Wang, Junxian Li, Xunzhi Wang, Jiatong Li, Jianbo Wu, Jingdi Lei, Haonan He, Peng Ye, Shufei Zhang, Wanli Ouyang, Yuqiang Li, Dongzhan Zhou. 2025-05-30. Control-R: Towards controllable test-time scaling. https://arxiv.org/abs/2506.00189

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