@inproceedings{gandhi-etal-2025-empirical,
    title = "An Empirical Study on Strong-Weak Model Collaboration for Repo-level Code Generation",
    author = "Gandhi, Shubham  and
      Naik, Atharva  and
      Xie, Yiqing  and
      Rose, Carolyn",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1043/",
    pages = "20678--20697",
    ISBN = "979-8-89176-332-6",
    abstract = "We study cost-efficient collaboration between strong and weak language models for repository-level code generation, where the weak model handles simpler tasks at lower cost, and the most challenging tasks are delegated to the strong model. While many works propose architectures for this task, few analyze performance relative to cost. We evaluate a broad spectrum of collaboration strategies: context-based, pipeline-based, and dynamic, on GitHub issue resolution. Our most effective collaborative strategy achieves equivalent performance to the strong model while reducing the cost by 40{\%}. Based on our findings, we offer actionable guidelines for choosing collaboration strategies under varying budget and performance constraints. Our results show that strong{--}weak collaboration substantially boosts the weak model{'}s performance at a fraction of the cost, pipeline and context-based methods being most efficient."
}