Control-R: Towards controllable test-time scaling
Authors: name: Di Zhang, name: Weida Wang, name: Junxian Li, name: Xunzhi Wang, name: Jiatong Li, name: Jianbo Wu, name: Jingdi Lei, name: Haonan He, name: Peng Ye, name: Shufei Zhang, name: Wanli Ouyang, name: Yuqiang Li, name: Dongzhan Zhou

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.