title: Control-R: Towards controllable test-time scaling
summary: 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.
paper_authors._id: 6840ee2f1d13437e974089ac
paper_authors.name: Di Zhang
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-06-05T01:09:04.306Z
paper_authors._id: 64bce15bafd1e46c5504ad38
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paper_authors.fullname: Di Zhang
paper_authors.type: user
paper_authors.user: di-zhang-fdu
paper_authors._id: 6840ee2f1d13437e974089ad
paper_authors.name: Weida Wang
paper_authors._id: 6840ee2f1d13437e974089ae
paper_authors.name: Junxian Li
paper_authors._id: 6840ee2f1d13437e974089af
paper_authors.name: Xunzhi Wang
paper_authors._id: 6840ee2f1d13437e974089b0
paper_authors.name: Jiatong Li
paper_authors._id: 6840ee2f1d13437e974089b1
paper_authors.name: Jianbo Wu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-05T07:27:21.086Z
paper_authors._id: 6407d3d3a7bc7c3865addc4b
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6407d3d3a7bc7c3865addc4b/NEMP6xShRq-5H_rQ5ganY.jpeg
paper_authors.fullname: Jianbo Wu
paper_authors.type: user
paper_authors.user: jwu323
paper_authors._id: 6840ee2f1d13437e974089b2
paper_authors.name: Jingdi Lei
paper_authors._id: 6840ee2f1d13437e974089b3
paper_authors.name: Haonan He
paper_authors._id: 6840ee2f1d13437e974089b4
paper_authors.name: Peng Ye
paper_authors._id: 6840ee2f1d13437e974089b5
paper_authors.name: Shufei Zhang
paper_authors._id: 6840ee2f1d13437e974089b6
paper_authors.name: Wanli Ouyang
paper_authors._id: 6840ee2f1d13437e974089b7
paper_authors.name: Yuqiang Li
paper_authors._id: 6840ee2f1d13437e974089b8
paper_authors.name: Dongzhan Zhou
paper_title: Control-R: Towards controllable test-time scaling
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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