@inproceedings{wei-etal-2026-unicreative,
    title = "{U}ni{C}reative: Unifying Long-form Logic and Short-form Sparkle via Reference-Free Reinforcement Learning",
    author = "Wei, Xiaolong  and
      Zhu, Zerun  and
      Niu, Simin  and
      Zhang, Xingyu  and
      Yu, Peiying  and
      Xiao, Changxuan  and
      Li, Yuchen  and
      Yang, Jicheng  and
      Zhao, Zhejun  and
      Meng, Chong  and
      Xia, Long  and
      Shi, Daiting",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1179/",
    pages = "23563--23583",
    ISBN = "979-8-89176-395-1",
    abstract = "A fundamental challenge in creative writing lies in reconciling the inherent tension between maintaining global coherence in long-form narratives and preserving local expressiveness in short-form texts. While long-context generation necessitates explicit macroscopic planning, short-form creativity often demands spontaneous, constraint-free expression. Existing alignment paradigms, however, typically employ static reward signals and rely heavily on high-quality supervised data, which is costly and difficult to scale. To address this, we propose UniCreative, a unified reference-free reinforcement learning framework. We first introduce AC-GenRM, an adaptive constraint-aware reward model that dynamically synthesizes query-specific criteria to provide fine-grained preference judgments. Leveraging these signals, we propose ACPO, a policy optimization algorithm that aligns models with human preferences across both content quality and structural paradigms without supervised fine-tuning and ground-truth references. Empirical results demonstrate that AC-GenRM aligns closely with expert evaluations, while ACPO significantly enhances performance across diverse writing tasks. Crucially, our analysis reveals an emergent meta-cognitive ability: the model learns to autonomously differentiate between tasks requiring rigorous planning and those favoring direct generation, validating the effectiveness of our direct alignment approach."
}