@inproceedings{sun-etal-2026-swe-world,
title = "{SWE}-World: Building Software Engineering Agents in Docker-Free Environments",
author = "Sun, Shuang and
Song, Huatong and
Huang, Lisheng and
Jiang, Jinhao and
Le, Ran and
Lv, Zhihao and
Chen, Zongchao and
Hu, Yiwen and
Luo, Wenyang and
Zhao, Xin and
Xu, Hongteng and
Wen, Ji-Rong",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1937/",
doi = "10.18653/2026.findings-emnlp.1937",
pages = "41316--41340",
ISBN = "979-8-89176-462-0",
abstract = "Recent advances in large language models (LLMs) have enabled software engineering agents to solve complex code modification tasks. However, most existing approaches rely on execution feedback from Docker-based environments, which require dependency-complete setup, program execution, and test validation, making agent training costly and difficult to scale. We propose SWE-World, a Docker-free framework that replaces containerized execution during downstream agent optimization with a learned surrogate environment. Trained on real agent-environment interaction traces, SWE-World predicts both step-level execution feedback and final test outcomes, preserving the standard agent-environment loop without repeatedly constructing or running Docker environments. This surrogate also enables test-time scaling (TTS) by scoring multiple candidate trajectories through simulated evaluation. Experiments on SWE-bench Verified show that SWE-World improves Qwen2.5-Coder-32B-Instruct from 6.2{\%} to 52.0{\%} with Docker-free SFT, 55.0{\%} with Docker-free RL, and 68.2{\%} with TTS. The code is available at \url{https://anonymous.4open.science/r/SWE-World-2D1E}."
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<abstract>Recent advances in large language models (LLMs) have enabled software engineering agents to solve complex code modification tasks. However, most existing approaches rely on execution feedback from Docker-based environments, which require dependency-complete setup, program execution, and test validation, making agent training costly and difficult to scale. We propose SWE-World, a Docker-free framework that replaces containerized execution during downstream agent optimization with a learned surrogate environment. Trained on real agent-environment interaction traces, SWE-World predicts both step-level execution feedback and final test outcomes, preserving the standard agent-environment loop without repeatedly constructing or running Docker environments. This surrogate also enables test-time scaling (TTS) by scoring multiple candidate trajectories through simulated evaluation. Experiments on SWE-bench Verified show that SWE-World improves Qwen2.5-Coder-32B-Instruct from 6.2% to 52.0% with Docker-free SFT, 55.0% with Docker-free RL, and 68.2% with TTS. The code is available at https://anonymous.4open.science/r/SWE-World-2D1E.</abstract>
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%0 Conference Proceedings
%T SWE-World: Building Software Engineering Agents in Docker-Free Environments
%A Sun, Shuang
%A Song, Huatong
%A Huang, Lisheng
%A Jiang, Jinhao
%A Le, Ran
%A Lv, Zhihao
%A Chen, Zongchao
%A Hu, Yiwen
%A Luo, Wenyang
%A Zhao, Xin
%A Xu, Hongteng
%A Wen, Ji-Rong
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F sun-etal-2026-swe-world
%X Recent advances in large language models (LLMs) have enabled software engineering agents to solve complex code modification tasks. However, most existing approaches rely on execution feedback from Docker-based environments, which require dependency-complete setup, program execution, and test validation, making agent training costly and difficult to scale. We propose SWE-World, a Docker-free framework that replaces containerized execution during downstream agent optimization with a learned surrogate environment. Trained on real agent-environment interaction traces, SWE-World predicts both step-level execution feedback and final test outcomes, preserving the standard agent-environment loop without repeatedly constructing or running Docker environments. This surrogate also enables test-time scaling (TTS) by scoring multiple candidate trajectories through simulated evaluation. Experiments on SWE-bench Verified show that SWE-World improves Qwen2.5-Coder-32B-Instruct from 6.2% to 52.0% with Docker-free SFT, 55.0% with Docker-free RL, and 68.2% with TTS. The code is available at https://anonymous.4open.science/r/SWE-World-2D1E.
%R 10.18653/2026.findings-emnlp.1937
%U https://aclanthology.org/2026.findings-emnlp.1937/
%U https://doi.org/10.18653/2026.findings-emnlp.1937
%P 41316-41340
Markdown (Informal)
[SWE-World: Building Software Engineering Agents in Docker-Free Environments](https://aclanthology.org/2026.findings-emnlp.1937/) (Sun et al., Findings 2026)
ACL
- Shuang Sun, Huatong Song, Lisheng Huang, Jinhao Jiang, Ran Le, Zhihao Lv, Zongchao Chen, Yiwen Hu, Wenyang Luo, Xin Zhao, Hongteng Xu, and Ji-Rong Wen. 2026. SWE-World: Building Software Engineering Agents in Docker-Free Environments. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 41316–41340, Budapest, Hungary. Association for Computational Linguistics.