What if you could train AI software engineers faster, without the heavy overhead of containers? Researchers from Peking University, Ant Groupe, and The University of Hong Kong present SWE-MiniSandbox. This novel, container-free method uses kernel-level isolation and lightweight pre-caching, eliminating bulky container images for reinforcement learning. It achieves comparable performance to container-based pipelines while reducing disk usage by 95% and environment setup time by 75%, making scalable RL training far more accessible for software engineering agents. SWE-MiniSandbox: Container-Free Reinforcement Learning for Building Software Engineering Agents Paper: arxiv.org/abs/2602.11210 Code: github.com/lblankl/SWE-MiniS… Docs: lblankl.github.io/SWE-MiniSa… Our report: mp.weixin.qq.com/s/NlQLprZmM… 📬 #PapersAccepted by Jiqizhixin

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