Training LLMs for Divide-and-Conquer Reasoning Elevates Test-Time Scalability
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arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866917242517585920 |
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| author | Liang, Xiao Li, Zhong-Zhi Lin, Zhenghao Jiang, Eric Hancheng Zhang, Hengyuan Shen, Yelong Chang, Kai-Wei Wu, Ying Nian Gong, Yeyun Chen, Weizhu |
| author_facet | Liang, Xiao Li, Zhong-Zhi Lin, Zhenghao Jiang, Eric Hancheng Zhang, Hengyuan Shen, Yelong Chang, Kai-Wei Wu, Ying Nian Gong, Yeyun Chen, Weizhu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_02477 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Training LLMs for Divide-and-Conquer Reasoning Elevates Test-Time Scalability Liang, Xiao Li, Zhong-Zhi Lin, Zhenghao Jiang, Eric Hancheng Zhang, Hengyuan Shen, Yelong Chang, Kai-Wei Wu, Ying Nian Gong, Yeyun Chen, Weizhu Computation and Language 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. |
| title | Training LLMs for Divide-and-Conquer Reasoning Elevates Test-Time Scalability |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2602.02477 |