@inproceedings{liang-etal-2026-training,
    title = "Training {LLM}s for Divide-and-Conquer Reasoning Elevates Test-Time Scalability",
    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",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1588/",
    pages = "34402--34427",
    ISBN = "979-8-89176-390-6",
    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 space. 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 problem conditioned on the subproblem solutions, with both decomposition and solution integrated into RL training. Under comparable training settings, 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. The code is available at the [provided link](https://github.com/MasterVito/DAC-RL)."
}