daVinci-Env: Open SWE Environment Synthesis at Scale
Training capable software engineering (SWE) agents demands large-scale, executable, and verifiable environments that provide dynamic feedback loops for iterative code editing, test execution, and solution refinement. However, existing open-source datasets remain limited in scale and repository diversity, while industrial solutions are opaque with unreleased infrastructure, creating a prohibitive barrier for most academic research groups. We present OpenSWE, the largest fully transparent framework for SWE agent training in Python, comprising 45,320 executable Docker environments spanning over 12.8k repositories, with all Dockerfiles, evaluation scripts, and infrastructure fully open-sourced for reproducibility. OpenSWE is built through a multi-agent synthesis pipeline deployed across a 64-node distributed cluster, automating repository exploration, Dockerfile construction, evaluation script generation, and iterative test analysis. Beyond scale, we propose a quality-centric filtering pipeline that characterizes the inherent difficulty of each environment, filtering out instances that are either unsolvable or insufficiently challenging and retaining only those that maximize learning efficiency. With 891K spent on environment construction and an additional 576K on trajectory sampling and difficulty-aware curation, the entire project represents a total investment of approximately $1.47 million, yielding about 13,000 curated trajectories from roughly 9,000 quality guaranteed environments. Extensive experiments validate OpenSWE's effectiveness: OpenSWE-32B and OpenSWE-72B achieve 62.4% and 66.0% on SWE-bench Verified, establishing SOTA among Qwen2.5 series. Moreover, SWE-focused training yields substantial out-of-domain improvements, including up to 12 points on mathematical reasoning and 5 points on science benchmarks, without degrading factual recall.
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Dayuan Fu
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2026-03-16T10:55:02.798Z
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Fu-Dayuan
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fudayuan
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Shenyu Wu
admin_assigned
2026-03-16T10:55:22.250Z
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Shenyu Wu
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Yunze Wu
admin_assigned
2026-03-16T10:55:28.705Z
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Yunze Wu
wyzmike
user
wyzmike
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Zerui Peng
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Yaxing Huang
admin_assigned
2026-03-16T10:55:43.600Z
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Yaxing Huang
Rookie-Noob-Newbie
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Jie Sun
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Ji Zeng
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2026-03-16T10:40:19.702Z
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Ji Zeng
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user
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Mohan Jiang
admin_assigned
2026-03-16T10:55:50.562Z
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Mohan Jiang (SII)
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user
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Lin Zhang
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Yukun Li
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Jiarui Hu
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Liming Liu
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Jinlong Hou
admin_assigned
2026-03-16T10:56:09.797Z
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jinlong hou
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Pengfei Liu
admin_assigned
2026-03-16T10:56:27.201Z
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Pengfei Liu
Pengfei
user
Pengfei
daVinci-Env: Open SWE Environment Synthesis at Scale
Training capable software engineering (SWE) agents demands large-scale, executable, and verifiable environments that provide dynamic feedback loops for iterative code editing, test execution, and solution refinement. However, existing open-source datasets remain limited in scale and repository diversity, while industrial solutions are opaque with unreleased infrastructure, creating a prohibitive barrier for most academic research groups. We present OpenSWE, the largest fully transparent framework for SWE agent training in Python, comprising 45,320 executable Docker environments spanning over 12.8k repositories, with all Dockerfiles, evaluation scripts, and infrastructure fully open-sourced for reproducibility. OpenSWE is built through a multi-agent synthesis pipeline deployed across a 64-node distributed cluster, automating repository exploration, Dockerfile construction, evaluation script generation, and iterative test analysis. Beyond scale, we propose a quality-centric filtering pipeline that characterizes the inherent difficulty of each environment, filtering out instances that are either unsolvable or insufficiently challenging and retaining only those that maximize learning efficiency. With 891K spent on environment construction and an additional 576K on trajectory sampling and difficulty-aware curation, the entire project represents a total investment of approximately $1.47 million, yielding about 13,000 curated trajectories from roughly 9,000 quality guaranteed environments. Extensive experiments validate OpenSWE's effectiveness: OpenSWE-32B and OpenSWE-72B achieve 62.4% and 66.0% on SWE-bench Verified, establishing SOTA among Qwen2.5 series. Moreover, SWE-focused training yields substantial out-of-domain improvements, including up to 12 points on mathematical reasoning and 5 points on science benchmarks, without degrading factual recall.
OpenSWE presents the largest transparent framework for software engineering agent training, featuring 45,320 executable environments and achieving state-of-the-art performance on SWE-bench Verified.
software engineering agents executable environments Docker environments multi-agent synthesis pipeline distributed cluster quality-centric filtering pipeline SWE-bench Verified Qwen2.5 series mathematical reasoning science benchmarks factual recall