Control-R: Towards controllable test-time scaling
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arXiv
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| Autori principali: | , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908387850059776 |
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| author | Zhang, Di Wang, Weida Li, Junxian Wang, Xunzhi Li, Jiatong Wu, Jianbo Lei, Jingdi He, Haonan Ye, Peng Zhang, Shufei Ouyang, Wanli Li, Yuqiang Zhou, Dongzhan |
| author_facet | Zhang, Di Wang, Weida Li, Junxian Wang, Xunzhi Li, Jiatong Wu, Jianbo Lei, Jingdi He, Haonan Ye, Peng Zhang, Shufei Ouyang, Wanli Li, Yuqiang Zhou, Dongzhan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00189 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Control-R: Towards controllable test-time scaling Zhang, Di Wang, Weida Li, Junxian Wang, Xunzhi Li, Jiatong Wu, Jianbo Lei, Jingdi He, Haonan Ye, Peng Zhang, Shufei Ouyang, Wanli Li, Yuqiang Zhou, Dongzhan Artificial Intelligence Computation and Language 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. |
| title | Control-R: Towards controllable test-time scaling |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2506.00189 |