@inproceedings{cao-etal-2026-dpwriter,
    title = "{DPW}riter: Reinforcement Learning with Diverse Planning Branching for Creative Writing",
    author = "Cao, Qian  and
      Liu, Yahui  and
      Bi, Wei  and
      Zhao, Yi  and
      Song, Ruihua  and
      Wang, Xiting  and
      Tang, Ruiming  and
      Zhou, Guorui  and
      Li, Han",
    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.647/",
    pages = "14224--14250",
    ISBN = "979-8-89176-390-6",
    abstract = "Reinforcement learning (RL)-based enhancement of large language models (LLMs) often leads to reduced output diversity, undermining their utility in open-ended tasks like creative writing. Current methods lack explicit mechanisms for guiding diverse exploration and instead prioritize optimization efficiency and performance over diversity. This paper proposes an RL framework structured around a semi-structured long Chain-of-Thought (CoT), in which the generation process is decomposed into explicitly planned intermediate steps. We introduce a Diverse Planning Branching method that strategically introduces divergence at the planning phase based on diversity variation, alongside a group-aware diversity reward to encourage distinct trajectories. Experimental results on creative writing benchmarks demonstrate that our approach significantly improves output diversity without compromising generation quality, consistently outperforming existing baselines."
}