SWE-Master: Unleashing the Potential of Software Engineering Agents via Post-Training
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.
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Huatong Song
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2026-02-10T09:08:02.704Z
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Song Huatong
XXsongLALA
user
XXsongLALA
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Lisheng Huang
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Shuang Sun
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Jinhao Jiang
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Ran Le
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Daixuan Cheng
claimed_verified
2026-02-04T12:27:42.035Z
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Daixuan Cheng
daixuancheng
user
daixuancheng
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Guoxin Chen
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Yiwen Hu
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Zongchao Chen
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Wayne Xin Zhao
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Yang Song
6982d3ec9084cb4f0ecb57fa
Tao Zhang
6982d3ec9084cb4f0ecb57fb
Ji-Rong Wen
SWE-Master: Unleashing the Potential of Software Engineering Agents via Post-Training
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.
SWE-Master presents a reproducible framework for developing software engineering agents through systematic optimization across multiple stages of agent development, achieving superior performance on software task resolution benchmarks.
post-training framework teacher-trajectory synthesis data curation long-horizon SFT RL with real execution feedback inference framework design SWE-bench Verified resolve rate test-time scaling LLM-based environment feedback