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  Model/Data associated with Paper:
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- **Training Software Engineering Agents and Verifiers with SWE-Gym**
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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+ <h1 align="center"> Training Software Engineering Agents and Verifiers with SWE-Gym </h1>
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+ <p align="center">
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+ <a href="https://www.jiayipan.com/" style="text-decoration: none;">Jiayi Pan<sup>*,1</sup></a>,
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+ <a href="https://xwang.dev/" style="text-decoration: none;">Xingyao Wang<sup>*,2</sup></a>,
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+ <a href="https://www.phontron.com/" style="text-decoration: none;">Graham Neubig<sup>3</sup></a>,
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+ <a href="https://www.cs.toronto.edu/~ndjaitly/" style="text-decoration: none;">Navdeep Jaitly<sup>4</sup></a>,
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+ <a href="https://blender.cs.illinois.edu/hengji.html" style="text-decoration: none;">Ji Heng<sup>2</sup></a>,
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+ <a href="https://www.alanesuhr.com/" style="text-decoration: none;">Alane Suhr<sup>^,1</sup></a>,
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+ <a href="https://dreasysnail.github.io/" style="text-decoration: none;">Yizhe Zhang<sup>^,4</sup></a>
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+ </p>
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+ <p align="center">
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+ <sup>1</sup>UC Berkeley, <sup>2</sup>UIUC, <sup>3</sup>CMU, <sup>4</sup>Apple </br>
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+ <sub><sup>*</sup>Equal contribution, <sup>^</sup>Equal supervision</sub>
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+ </p>
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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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