{"ID":2822832,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2601.01426","arxiv_id":"2601.01426","title":"SWE-Lego: Pushing the Limits of Supervised Fine-tuning for Software Issue Resolving","abstract":"We present SWE-Lego, a supervised fine-tuning (SFT) recipe designed to achieve state-ofthe-art performance in software engineering (SWE) issue resolving. In contrast to prevalent methods that rely on complex training paradigms (e.g., mid-training, SFT, reinforcement learning, and their combinations), we explore how to push the limits of a lightweight SFT-only approach for SWE tasks. SWE-Lego comprises three core building blocks, with key findings summarized as follows: 1) the SWE-Lego dataset, a collection of 32k highquality task instances and 18k validated trajectories, combining real and synthetic data to complement each other in both quality and quantity; 2) a refined SFT procedure with error masking and a difficulty-based curriculum, which demonstrably improves action quality and overall performance. Empirical results show that with these two building bricks alone,the SFT can push SWE-Lego models to state-of-the-art performance among open-source models of comparable size on SWE-bench Verified: SWE-Lego-Qwen3-8B reaches 42.2%, and SWE-Lego-Qwen3-32B attains 52.6%. 3) We further evaluate and improve test-time scaling (TTS) built upon the SFT foundation. Based on a well-trained verifier, SWE-Lego models can be significantly boosted--for example, 42.2% to 49.6% and 52.6% to 58.8% under TTS@16 for the 8B and 32B models, respectively.","short_abstract":"We present SWE-Lego, a supervised fine-tuning (SFT) recipe designed to achieve state-ofthe-art performance in software engineering (SWE) issue resolving. In contrast to prevalent methods that rely on complex training paradigms (e.g., mid-training, SFT, reinforcement learning, and their combinations), we explore how to...","url_abs":"https://arxiv.org/abs/2601.01426","url_pdf":"https://arxiv.org/pdf/2601.01426v2","authors":"[\"Chaofan Tao\",\"Jierun Chen\",\"Yuxin Jiang\",\"Kaiqi Kou\",\"Shaowei Wang\",\"Ruoyu Wang\",\"Xiaohui Li\",\"Sidi Yang\",\"Yiming Du\",\"Jianbo Dai\",\"Zhiming Mao\",\"Xinyu Wang\",\"Lifeng Shang\",\"Haoli Bai\"]","published":"2026-01-04T08:07:27Z","proceeding":"cs.SE","tasks":"[\"cs.SE\",\"cs.CL\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}