Hybrid-Gym: Training Coding Agents to Generalize Across Tasks
arXiv:2602.16819v1 Announce Type: cross 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-Be
arXiv:2602.16819v1 Announce Type: cross 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. 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.
Executive Summary
This article proposes a novel training environment, Hybrid-Gym, designed to enhance the generalizability of coding agents across various tasks. By decomposing trajectories into fine-grained components, the authors identify transferable skills shared across diverse tasks and derive principles for designing auxiliary training tasks. Hybrid-Gym is a set of scalable synthetic tasks that significantly improve the performance of coding agents on real-world tasks, including SWE-Bench Verified, SWT-Bench Verified, and Commit-0 Lite. The authors demonstrate that Hybrid-Gym complements existing datasets, such as SWE-Play, and provides a potential solution to the current limitations of training coding agents. The results show a substantial improvement in the performance of coding agents, with a 25.4% absolute gain on SWE-Bench Verified, indicating the potential of Hybrid-Gym in real-world applications.
Key Points
- ▸ Hybrid-Gym is a novel training environment designed to enhance the generalizability of coding agents.
- ▸ The authors identify transferable skills shared across diverse tasks by decomposing trajectories into fine-grained components.
- ▸ Hybrid-Gym consists of scalable synthetic tasks that significantly improve the performance of coding agents on real-world tasks.
Merits
Strength in Design
The authors' design of Hybrid-Gym is well-structured and effective in improving the generalizability of coding agents. The use of fine-grained components to identify transferable skills is a significant contribution to the field.
Demerits
Limited Real-World Evaluation
The article primarily evaluates the performance of Hybrid-Gym on synthetic tasks and a limited set of real-world tasks, which may not fully represent the complexity of real-world scenarios.
Expert Commentary
The article presents a significant contribution to the field of AI, particularly in the area of coding agents and transfer learning. The authors' design of Hybrid-Gym is well-structured and effective in improving the generalizability of coding agents. However, the article's focus on synthetic tasks and a limited set of real-world tasks may limit its generalizability. Furthermore, the article's findings have significant practical implications for the improvement of coding agents in real-world applications. As AI systems become increasingly prevalent in software development and maintenance, the development of Hybrid-Gym has the potential to significantly impact the field.
Recommendations
- ✓ Future research should focus on evaluating the performance of Hybrid-Gym on a broader range of real-world tasks to fully demonstrate its effectiveness.
- ✓ The development of Hybrid-Gym should be further explored in the context of other AI applications, such as natural language processing and computer vision.
