Hybrid-Gym: Training Coding Agents to Generalize Across Tasks
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arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866914338541928448 |
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| author | Xie, Yiqing Liu, Emmy Zhang, Gaokai Kotalwar, Nachiket Gandhi, Shubham Acharya, Sathwik Wang, Xingyao Rose, Carolyn Neubig, Graham Fried, Daniel |
| author_facet | Xie, Yiqing Liu, Emmy Zhang, Gaokai Kotalwar, Nachiket Gandhi, Shubham Acharya, Sathwik Wang, Xingyao Rose, Carolyn Neubig, Graham Fried, Daniel |
| contents | When assessing the quality of coding agents, predominant benchmarks focus on solving single issues on GitHub, such as SWE-Bench. In contrast, in real use, these agents solve more various and complex tasks that involve other skills such as exploring codebases, testing software, and designing architecture. In this paper, we first characterize some transferable skills that are shared across diverse tasks by decomposing trajectories into fine-grained components, and derive a set of principles for designing auxiliary training tasks to teach language models these skills. Guided by these principles, we propose a training environment, Hybrid-Gym, consisting of a set of scalable synthetic tasks, such as function localization and dependency search. Experiments show that agents trained on our synthetic tasks effectively generalize to diverse real-world tasks that are not present in training, improving a base model by 25.4% absolute gain on SWE-Bench Verified, 7.9% on SWT-Bench Verified, and 5.1% on Commit-0 Lite. Hybrid-Gym also complements datasets built for the downstream tasks (e.g., improving SWE-Play by 4.9% on SWT-Bench Verified). Code available at: https://github.com/yiqingxyq/Hybrid-Gym. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_16819 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Hybrid-Gym: Training Coding Agents to Generalize Across Tasks Xie, Yiqing Liu, Emmy Zhang, Gaokai Kotalwar, Nachiket Gandhi, Shubham Acharya, Sathwik Wang, Xingyao Rose, Carolyn Neubig, Graham Fried, Daniel Software Engineering Computation and Language Machine Learning When assessing the quality of coding agents, predominant benchmarks focus on solving single issues on GitHub, such as SWE-Bench. In contrast, in real use, these agents solve more various and complex tasks that involve other skills such as exploring codebases, testing software, and designing architecture. In this paper, we first characterize some transferable skills that are shared across diverse tasks by decomposing trajectories into fine-grained components, and derive a set of principles for designing auxiliary training tasks to teach language models these skills. Guided by these principles, we propose a training environment, Hybrid-Gym, consisting of a set of scalable synthetic tasks, such as function localization and dependency search. Experiments show that agents trained on our synthetic tasks effectively generalize to diverse real-world tasks that are not present in training, improving a base model by 25.4% absolute gain on SWE-Bench Verified, 7.9% on SWT-Bench Verified, and 5.1% on Commit-0 Lite. Hybrid-Gym also complements datasets built for the downstream tasks (e.g., improving SWE-Play by 4.9% on SWT-Bench Verified). Code available at: https://github.com/yiqingxyq/Hybrid-Gym. |
| title | Hybrid-Gym: Training Coding Agents to Generalize Across Tasks |
| topic | Software Engineering Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2602.16819 |