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[Jiayi Pan*](https://www.jiayipan.com/), [Xingyao Wang*](https://xwang.dev/), [Graham Neubig](https://www.phontron.com/), [Navdeep Jaitly](https://www.cs.toronto.edu/~ndjaitly/), [Ji Heng](https://blender.cs.illinois.edu/hengji.html), [Alane Suhr^](https://www.alanesuhr.com/), [Yizhe Zhang^](https://dreasysnail.github.io/)
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UC Berkeley, UIUC, CMU, Apple
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We present SWE-Gym, the first environment for training real-world software engineering agents. We use it to train strong LM agents that achieve state-of-the-art open results on SWE-Bench, with early, promising scaling characteristics as we increase training and inference-time compute.
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[Jiayi Pan*](https://www.jiayipan.com/), [Xingyao Wang*](https://xwang.dev/), [Graham Neubig](https://www.phontron.com/), [Navdeep Jaitly](https://www.cs.toronto.edu/~ndjaitly/), [Ji Heng](https://blender.cs.illinois.edu/hengji.html), [Alane Suhr^](https://www.alanesuhr.com/), [Yizhe Zhang^](https://dreasysnail.github.io/)
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UC Berkeley, UIUC, CMU, Apple | *, ^ denotes equal contribution
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We present SWE-Gym, the first environment for training real-world software engineering agents. We use it to train strong LM agents that achieve state-of-the-art open results on SWE-Bench, with early, promising scaling characteristics as we increase training and inference-time compute.
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