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Training LLMs for Divide-and-Conquer Reasoning

Xiao Liang, Zhong-Zhi Li, Zhenghao Lin, Eric Hanchen Jiang, Hengyuan Zhang, Yelong Shen, Kai-Wei Chang, Ying Nian Wu, Yeyun Gong, and Weizhu Chen, in ACL, 2026.

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Abstract

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.

Bib Entry

@inproceedings{jiang2026divide,
  title = {Training LLMs for Divide-and-Conquer Reasoning},
  author = {Liang, Xiao and Li, Zhong-Zhi and Lin, Zhenghao and Jiang, Eric Hanchen and Zhang, Hengyuan and Shen, Yelong and Chang, Kai-Wei and Wu, Ying Nian and Gong, Yeyun and Chen, Weizhu},
  booktitle = {ACL},
  year = {2026}
}

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