Training LLMs for Divide-and-Conquer Reasoning Elevates Test-Time Scalability
Large language models (LLMs) have demonstrated strong reasoning capabilities through step-by-step chain-of-thought (CoT) reasoning. Nevertheless, at the limits of model capability, CoT often proves insufficient, and its strictly sequential nature constrains test-time scalability. A potential alternative is divide-and-conquer (DAC) reasoning, which decomposes a complex problem into subproblems to facilitate more effective exploration of the solution. Although promising, our analysis reveals a fundamental misalignment between general-purpose post-training and DAC-style inference, which limits the model's capacity to fully leverage this potential. To bridge this gap and fully unlock LLMs' reasoning capabilities on the most challenging tasks, we propose an end-to-end reinforcement learning (RL) framework to enhance their DAC-style reasoning capacity. At each step, the policy decomposes a problem into a group of subproblems, solves them sequentially, and addresses the original one conditioned on the subproblem solutions, with both decomposition and solution integrated into RL training. Under comparable training, our DAC-style framework endows the model with a higher performance ceiling and stronger test-time scalability, surpassing CoT by 8.6% in Pass@1 and 6.3% in Pass@32 on competition-level benchmarks.
69825f069084cb4f0ecb5524
Xiao Liang
claimed_verified
2026-02-04T12:29:51.846Z
6560763e152b659e623865ae
https://cdn-avatars.huggingface.co/v1/production/uploads/6560763e152b659e623865ae/cTT2jGnPU_8XMrUTvqZ2h.jpeg
Xiao Liang
MasterVito
user
MasterVito
69825f069084cb4f0ecb5525
Zhong-Zhi Li
69825f069084cb4f0ecb5526
Zhenghao Lin
69825f069084cb4f0ecb5527
Eric Hancheng Jiang
69825f069084cb4f0ecb5528
Hengyuan Zhang
69825f069084cb4f0ecb5529
Yelong Shen
69825f069084cb4f0ecb552a
Kai-Wei Chang
69825f069084cb4f0ecb552b
Ying Nian Wu
69825f069084cb4f0ecb552c
Yeyun Gong
69825f069084cb4f0ecb552d
Weizhu Chen
Training LLMs for Divide-and-Conquer Reasoning Elevates Test-Time Scalability
Large language models (LLMs) have demonstrated strong reasoning capabilities through step-by-step chain-of-thought (CoT) reasoning. Nevertheless, at the limits of model capability, CoT often proves insufficient, and its strictly sequential nature constrains test-time scalability. A potential alternative is divide-and-conquer (DAC) reasoning, which decomposes a complex problem into subproblems to facilitate more effective exploration of the solution. Although promising, our analysis reveals a fundamental misalignment between general-purpose post-training and DAC-style inference, which limits the model's capacity to fully leverage this potential. To bridge this gap and fully unlock LLMs' reasoning capabilities on the most challenging tasks, we propose an end-to-end reinforcement learning (RL) framework to enhance their DAC-style reasoning capacity. At each step, the policy decomposes a problem into a group of subproblems, solves them sequentially, and addresses the original one conditioned on the subproblem solutions, with both decomposition and solution integrated into RL training. Under comparable training, our DAC-style framework endows the model with a higher performance ceiling and stronger test-time scalability, surpassing CoT by 8.6% in Pass@1 and 6.3% in Pass@32 on competition-level benchmarks.
An end-to-end reinforcement learning framework enhances large language models' reasoning capabilities by implementing divide-and-conquer strategies that outperform traditional chain-of-thought reasoning on challenging benchmarks.
chain-of-thought reasoning divide-and-conquer reasoning reinforcement learning large language models policy decomposition solution integration test-time scalability Pass@1 Pass@32