![[2602.16819] Hybrid-Gym: Training Coding Agents to Generalize Across Tasks](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.16819] Hybrid-Gym: Training Coding Agents to Generalize Across Tasks
Summary
The paper presents Hybrid-Gym, a training environment designed to enhance coding agents' ability to generalize across various software engineering tasks, demonstrating significant performance improvements on established benchmarks.
Why It Matters
As coding agents become integral to software development, their ability to tackle diverse tasks is crucial. Hybrid-Gym addresses the limitations of current benchmarks by introducing a training framework that fosters transferable skills, thereby improving the practical utility of AI in coding.
Key Takeaways
- Hybrid-Gym provides a scalable environment for training coding agents.
- Agents trained in Hybrid-Gym show improved performance on real-world tasks.
- The framework emphasizes transferable skills essential for diverse coding challenges.
Computer Science > Software Engineering arXiv:2602.16819 (cs) [Submitted on 18 Feb 2026] Title:Hybrid-Gym: Training Coding Agents to Generalize Across Tasks Authors:Yiqing Xie, Emmy Liu, Gaokai Zhang, Nachiket Kotalwar, Shubham Gandhi, Sathwik Acharya, Xingyao Wang, Carolyn Rose, Graham Neubig, Daniel Fried View a PDF of the paper titled Hybrid-Gym: Training Coding Agents to Generalize Across Tasks, by Yiqing Xie and 9 other authors View PDF HTML (experimental) Abstract: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. Hybr...
