@inproceedings{ke-etal-2026-qimeng,
    title = "{Q}i{M}eng-{PR}epair: Precise Code Repair via Edit-Aware Reward Optimization",
    author = "Ke, Changxin  and
      Zhang, Rui  and
      Guo, Jiaming  and
      Wen, Yuanbo  and
      Ding, Li  and
      Wang, Shuo  and
      Zhu, Xuyuan  and
      Peng, Xiong  and
      Huang, Di  and
      Du, Zidong  and
      Hu, Xing  and
      Guo, Qi  and
      Chen, Yunji",
    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.1343/",
    pages = "29114--29129",
    ISBN = "979-8-89176-390-6",
    abstract = "Large Language Models (LLMs) achieve strong program repair performance but often suffer from over-editing, where excessive modifications overwrite correct code and hinder bug localization. We systematically quantify its impact and introduce precise repair task, which maximizes reuse of correct code while fixing only buggy parts. Building on this insight, we propose PRepair, a framework that mitigates over-editing and improves repair accuracy. PRepair has two components: Self-Breaking, which generates diverse buggy programs via controlled bug injection and min{--}max sampling, and Self-Repairing, which trains models with Edit-Aware Group Relative Policy Optimization (EA-GRPO) using an edit-aware reward to encourage minimal yet correct edits. Experiments show that PRepair improves repair precision by up to 31.4{\%} under $\mathrm{fix}_1@1$, a metric that jointly considers repair correctness and extent, and significantly increases decoding throughput when combined with speculative editing, demonstrating its potential for precise and practical code repair."
}