SWE-MiniSandbox: Container-Free Reinforcement Learning for Building Software Engineering Agents

• URL: http://arxiv.org/abs/2602.11210v1
• Date: Wed, 11 Feb 2026 02:33:04 GMT
• Title: SWE-MiniSandbox: Container-Free Reinforcement Learning for Building Software Engineering Agents
• Authors: Danlong Yuan, Wei Wu, Zhengren Wang, Xueliang Zhao, Huishuai Zhang, Dongyan Zhao,
• Abstract summary: Reinforcement learning (RL) has become a key paradigm for training software engineering (SWE) agents.<n>Existing pipelines typically rely on per-task containers for isolation.<n>We propose SWE-MiniSandbox, a container-free method that enables scalable RL training of SWE agents without sacrificing isolation.
• Score: 44.0779548943909
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement learning (RL) has become a key paradigm for training software engineering (SWE) agents, but existing pipelines typically rely on per-task containers for isolation. At scale, pre-built container images incur substantial storage overhead, slow environment setup, and require container-management privileges. We propose SWE-MiniSandbox, a lightweight, container-free method that enables scalable RL training of SWE agents without sacrificing isolation. Instead of relying on per-instance containers, SWE-MiniSandbox executes each task in an isolated workspace backed by kernel-level mechanisms, substantially reducing system overhead. It leverages lightweight environment pre-caching techniques to eliminate the need for bulky container images. As a result, our approach lowers disk usage to approximately 5\% of that required by container-based pipelines and reduces environment preparation time to about 25\% of the container baseline. Empirical results demonstrate that SWE-MiniSandbox achieves evaluation performance comparable to standard container-based pipelines. By removing the dependency on heavy container infrastructure, SWE-MiniSandbox offers a practical and accessible foundation for scaling RL-based SWE agents, particularly in resource-constrained research environments.

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