SWE-World: Building Software Engineering Agents in Docker-Free Environments
Recent advances in large language models (LLMs) have enabled software engineering agents to tackle complex code modification tasks. Most existing approaches rely on execution feedback from containerized environments, which require dependency-complete setup and physical execution of programs and tests. While effective, this paradigm is resource-intensive and difficult to maintain, substantially complicating agent training and limiting scalability. We propose SWE-World, a Docker-free framework that replaces physical execution environments with a learned surrogate for training and evaluating software engineering agents. SWE-World leverages LLM-based models trained on real agent-environment interaction data to predict intermediate execution outcomes and final test feedback, enabling agents to learn without interacting with physical containerized environments. This design preserves the standard agent-environment interaction loop while eliminating the need for costly environment construction and maintenance during agent optimization and evaluation. Furthermore, because SWE-World can simulate the final evaluation outcomes of candidate trajectories without real submission, it enables selecting the best solution among multiple test-time attempts, thereby facilitating effective test-time scaling (TTS) in software engineering tasks. Experiments on SWE-bench Verified demonstrate that SWE-World raises Qwen2.5-Coder-32B from 6.2\% to 52.0\% via Docker-free SFT, 55.0\% with Docker-free RL, and 68.2\% with further TTS. The code is available at https://github.com/RUCAIBox/SWE-World
6982d38d9084cb4f0ecb57df
Shuang Sun
6982d38d9084cb4f0ecb57e0
Huatong Song
claimed_verified
2026-02-10T09:08:05.057Z
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/avatars/801043dac0caae90bbca8c9d3e2e203b.svg
Song Huatong
XXsongLALA
user
XXsongLALA
6982d38d9084cb4f0ecb57e1
Lisheng Huang
6982d38d9084cb4f0ecb57e2
Jinhao Jiang
6982d38d9084cb4f0ecb57e3
Ran Le
6982d38d9084cb4f0ecb57e4
Zhihao Lv
6982d38d9084cb4f0ecb57e5
Zongchao Chen
6982d38d9084cb4f0ecb57e6
Yiwen Hu
6982d38d9084cb4f0ecb57e7
Wenyang Luo
6982d38d9084cb4f0ecb57e8
Wayne Xin Zhao
6982d38d9084cb4f0ecb57e9
Yang Song
6982d38d9084cb4f0ecb57ea
Hongteng Xu
6982d38d9084cb4f0ecb57eb
Tao Zhang
6982d38d9084cb4f0ecb57ec
Ji-Rong Wen
SWE-World: Building Software Engineering Agents in Docker-Free Environments
Recent advances in large language models (LLMs) have enabled software engineering agents to tackle complex code modification tasks. Most existing approaches rely on execution feedback from containerized environments, which require dependency-complete setup and physical execution of programs and tests. While effective, this paradigm is resource-intensive and difficult to maintain, substantially complicating agent training and limiting scalability. We propose SWE-World, a Docker-free framework that replaces physical execution environments with a learned surrogate for training and evaluating software engineering agents. SWE-World leverages LLM-based models trained on real agent-environment interaction data to predict intermediate execution outcomes and final test feedback, enabling agents to learn without interacting with physical containerized environments. This design preserves the standard agent-environment interaction loop while eliminating the need for costly environment construction and maintenance during agent optimization and evaluation. Furthermore, because SWE-World can simulate the final evaluation outcomes of candidate trajectories without real submission, it enables selecting the best solution among multiple test-time attempts, thereby facilitating effective test-time scaling (TTS) in software engineering tasks. Experiments on SWE-bench Verified demonstrate that SWE-World raises Qwen2.5-Coder-32B from 6.2\% to 52.0\% via Docker-free SFT, 55.0\% with Docker-free RL, and 68.2\% with further TTS. The code is available at https://github.com/RUCAIBox/SWE-World
A Docker-free framework replaces physical execution environments with learned surrogates for training software engineering agents, enabling efficient training and test-time scaling without costly container setup.
large language models software engineering agents Docker-free framework surrogate models agent-environment interaction test-time scaling SWE-bench Qwen2.5-Coder-32B