Control-R: Towards controllable test-time scaling
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
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2025-08-14T04:06:58.679Z
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Di Zhang
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Weida Wang
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2025-07-01T10:14:53.160Z
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Weida Wang
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Junxian Li
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Xunzhi Wang
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Jiatong Li
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Jiatong LI
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Jianbo Wu
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2025-06-05T07:27:21.086Z
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Jianbo Wu
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Jingdi Lei
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Haonan He
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Peng Ye
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Shufei Zhang
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Wanli Ouyang
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Yuqiang Li
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Dongzhan Zhou
Control-R: Towards controllable test-time scaling
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.
A novel approach using Reasoning Control Fields (RCF) and Conditional Distillation Finetuning (CDF) enables controllable reasoning in Large Reasoning Models (LRMs) to achieve state-of-the-art performance on complex tasks.
Reasoning Control Fields RCF long chain-of-thought reasoning Large Reasoning Models LRMs Control-R-4K dataset Conditional Distillation Finetuning CDF AIME2024 MATH500 controllable Long CoT reasoning L-CoT