In this technical report, we present SWE-Master, an open-source and fully reproducible post-training framework for building effective software engineering agents. SWE-Master systematically explores the complete agent development pipeline, including teacher-trajectory synthesis and data curation, long-horizon SFT, RL with real execution feedback, and inference framework design. Starting from an open-source base model with limited initial SWE capability, SWE-Master demonstrates how systematical optimization method can elicit strong long-horizon SWE task solving abilities. We evaluate SWE-Master on SWE-bench Verified, a standard benchmark for realistic software engineering tasks. Under identical experimental settings, our approach achieves a resolve rate of 61.4\% with Qwen2.5-Coder-32B, substantially outperforming existing open-source baselines. By further incorporating test-time scaling~(TTS) with LLM-based environment feedback, SWE-Master reaches 70.8\% at TTS@8, demonstrating a strong performance potential. SWE-Master provides a practical and transparent foundation for advancing reproducible research on software engineering agents. The code is available at https://github.com/RUCAIBox/SWE-Master.

翻译：在本技术报告中，我们介绍了SWE-Master——一个开源且完全可复现的后训练框架，用于构建高效的软件工程智能体。SWE-Master系统地探索了完整的智能体开发流程，包括教师轨迹合成与数据整理、长视野监督微调、基于真实执行反馈的强化学习以及推理框架设计。从一个初始软件工程能力有限的开源基础模型出发，SWE-Master展示了系统化的优化方法如何激发出强大的长视野软件工程任务解决能力。我们在SWE-bench Verified（一个面向现实软件工程任务的标准基准测试）上对SWE-Master进行了评估。在相同的实验设置下，我们的方法使用Qwen2.5-Coder-32B模型取得了61.4%的解决率，显著超越了现有的开源基线模型。通过进一步结合基于大语言模型环境反馈的测试时扩展技术，SWE-Master在TTS@8设置下达到了70.8%的解决率，展现出强劲的性能潜力。SWE-Master为推进软件工程智能体领域的可复现研究提供了一个实用且透明的基石。相关代码已发布于https://github.com/RUCAIBox/SWE-Master。