@inproceedings{xu-etal-2026-coact,
    title = "{C}o{A}ct: Co-Active {LLM} Preference Learning with Human-{AI} Synergy",
    author = "Xu, Ruiyao  and
      Parmar, Mihir  and
      Yang, Tiankai  and
      Hu, Zhengyu  and
      Zhao, Yue  and
      Ding, Kaize",
    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.751/",
    pages = "16508--16525",
    ISBN = "979-8-89176-390-6",
    abstract = "Learning from preference-based feedback has become an effective approach for aligning LLMs across diverse tasks. However, high-quality human-annotated preference data remains expensive and scarce. Existing methods address this challenge through either self-rewarding, which scales by using purely AI-generated labels but risks unreliability, or active learning, which ensures quality through oracle annotation but cannot fully leverage unlabeled data. In this paper, we present CoAct, a novel framework that synergistically combines self-rewarding and active learning through strategic human-AI collaboration. CoAct leverages self-consistency to identify both reliable self-labeled data and samples requiring oracle verification. Additionally, oracle feedback guides the model to generate new instructions within its solvable capability. Evaluated on three reasoning benchmarks across two model families, CoAct achieves average improvements of +13.25{\%} on GSM8K, +8.19{\%} on MATH, and +13.16{\%} on WebInstruct, consistently outperforming all baselines."
}