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Softw. Eng. Methodol."],"published-print":{"date-parts":[[2026,2,28]]},"abstract":"<jats:p>\n                    Within the realm of software engineering, specialized tasks on code, such as program repair, present unique challenges, necessitating fine-tuning Large language models (LLMs) to unlock state-of-the-art performance. Fine-tuning approaches proposed in the literature for LLMs on program repair tasks generally overlook the need to reason about the logic behind code changes, beyond syntactic patterns in the data. High-performing fine-tuning experiments also usually come at very high computational costs. With\n                    <jats:sc>MORepair<\/jats:sc>\n                    , we propose a novel perspective on the learning focus of LLM fine-tuning for program repair: we not only adapt the LLM parameters to the syntactic nuances of the task of code transformation (objective \u278a), but we also specifically fine-tune the LLM with respect to the logical reason behind the code change in the training data (objective \u278b). Such a multi-objective fine-tuning will instruct LLMs to generate\n                    <jats:italic toggle=\"yes\">high-quality<\/jats:italic>\n                    patches.\n                  <\/jats:p>\n                  <jats:p>\n                    We apply\n                    <jats:sc>MORepair<\/jats:sc>\n                    to fine-tune four open-source LLMs with different sizes and architectures. Experimental results on function-level and repository-level repair benchmarks show that the implemented fine-tuning effectively boosts LLM repair performance by 11.4% to 56.0%. We further show that our fine-tuning strategy yields superior performance compared to the state-of-the-art approaches, including standard fine-tuning, Fine-tune-CoT, and RepairLLaMA.\n                  <\/jats:p>","DOI":"10.1145\/3735129","type":"journal-article","created":{"date-parts":[[2025,5,10]],"date-time":"2025-05-10T03:01:57Z","timestamp":1746846117000},"page":"1-38","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["<scp>MORepair<\/scp>\n                    : Teaching LLMs to Repair Code via Multi-Objective Fine-Tuning"],"prefix":"10.1145","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9270-730X","authenticated-orcid":false,"given":"Boyang","family":"Yang","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Yanshan University, Qinhuangdao, China and Jisuan Institute of Technology, Beijing JudaoYouda Network Technology Co. Ltd., Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8049-3997","authenticated-orcid":false,"given":"Haoye","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Computing and Information Systems, University of Melbourne, Melbourne, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2245-9133","authenticated-orcid":false,"given":"Jiadong","family":"Ren","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Yanshan University, Qinhuangdao, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3063-9425","authenticated-orcid":false,"given":"Hongyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Big Data and Software Engineering, Chongqing University, Chongqing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4052-475X","authenticated-orcid":false,"given":"Jacques","family":"Klein","sequence":"additional","affiliation":[{"name":"SnT, University of Luxembourg, Esch-sur-Alzette, Luxembourg"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7270-9869","authenticated-orcid":false,"given":"Tegawend\u00e9 F.","family":"Bissyand\u00e9","sequence":"additional","affiliation":[{"name":"SnT, University of Luxembourg, Esch-sur-Alzette, Luxembourg"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3931-060X","authenticated-orcid":false,"given":"Claire Le","family":"Goues","sequence":"additional","affiliation":[{"name":"School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5845-5601","authenticated-orcid":false,"given":"Shunfu","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Yanshan University, Qinhuangdao, China"}]}],"member":"320","published-online":{"date-parts":[[2026,1,21]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"Josh Achiam Steven Adler Sandhini Agarwal Lama Ahmad Ilge Akkaya Florencia Leoni Aleman Diogo Almeida Janko Altenschmidt Sam Altman Shyamal Anadkat et al. 2023. 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Retrieved from https:\/\/sebastianraschka.substack.com\/p\/practical-tips-for-finetuning-llms"},{"key":"e_1_3_1_47_2","unstructured":"Baptiste Roziere Jonas Gehring Fabian Gloeckle Sten Sootla Itai Gat Xiaoqing Ellen Tan Yossi Adi Jingyu Liu Tal Remez J\u00e9r\u00e9my Rapin et al. 2023. Code llama: Open foundation models for code. arXiv:2308.12950. Retrieved from https:\/\/arxiv.org\/abs\/2308.12950"},{"key":"e_1_3_1_48_2","unstructured":"Sebastian Ruder. 2017. An overview of multi-task learning in deep neural networks. arXiv:1706.05098. Retrieved from https:\/\/arxiv.org\/abs\/1706.05098"},{"key":"e_1_3_1_49_2","unstructured":"Andr\u00e9 Silva Sen Fang and Martin Monperrus. 2023. RepairLLaMA: Efficient representations and fine-tuned adapters for program repair. arXiv:2312.15698. Retrieved from https:\/\/arxiv.org\/abs\/2312.15698"},{"key":"e_1_3_1_50_2","doi-asserted-by":"crossref","unstructured":"Dominik Sobania Martin Briesch Carol Hanna and Justyna Petke. 2023. 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