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SWE-MiniSandbox: Container-Free Reinforcement Learning for Building Software Engineering Agents
arXiv – CS AI|Danlong Yuan, Wei Wu, Zhengren Wang, Xueliang Zhao, Huishuai Zhang, Dongyan Zhao||2 views
🤖AI Summary
Researchers introduced SWE-MiniSandbox, a container-free method for training software engineering AI agents using reinforcement learning that reduces disk usage to 5% and environment setup time to 25% of traditional container-based approaches. The system uses kernel-level isolation and lightweight pre-caching instead of bulky container images while maintaining comparable performance.
Key Takeaways
- →SWE-MiniSandbox eliminates the need for per-task containers in RL training of software engineering agents.
- →The system reduces disk usage to approximately 5% of container-based pipelines.
- →Environment preparation time is cut to about 25% of container baseline methods.
- →Performance remains comparable to standard container-based approaches despite the efficiency gains.
- →The solution makes RL-based SWE agent training more accessible in resource-constrained environments.
#reinforcement-learning#software-engineering#ai-agents#optimization#research#scalability#containerization#machine-learning
Read Original →via arXiv – CS AI
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