SWE-World: Building Software Engineering Agents in Docker-Free Environments

• URL: http://arxiv.org/abs/2602.03419v1
• Date: Tue, 03 Feb 2026 11:44:39 GMT
• Title: SWE-World: Building Software Engineering Agents in Docker-Free Environments
• Authors: Shuang Sun, Huatong Song, Lisheng Huang, Jinhao Jiang, Ran Le, Zhihao Lv, Zongchao Chen, Yiwen Hu, Wenyang Luo, Wayne Xin Zhao, Yang Song, Hongteng Xu, Tao Zhang, Ji-Rong Wen,
• Abstract summary: SWE-World is a Docker-free framework that replaces physical execution environments with a learned surrogate for training and evaluating software engineering agents.<n>We show 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.
• Score: 91.17484806743641
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

Related papers

• Agent World Model: Infinity Synthetic Environments for Agentic Reinforcement Learning [62.499592503950026]
  Large language model (LLM) have empowered autonomous agents to perform complex tasks that require multi-turn interactions with tools and environments.<n>We propose Agent World Model (AWM), a fully synthetic environment generation pipeline.<n>We scale to 1,000 environments covering everyday scenarios, in which agents can interact with rich toolsets.
  arXiv  Detail & Related papers  (2026-02-10T18:55:41Z)
• SWE-Master: Unleashing the Potential of Software Engineering Agents via Post-Training [78.37721886775215]
  We present SWE-Master, an open-source framework for building effective software engineering agents.<n>SWE-Master systematically explores the complete agent development pipeline, including teacher-trajectory synthesis and data curation.<n>We evaluate SWE-Master on SWE-bench Verified, a standard benchmark for realistic software engineering tasks.
  arXiv  Detail & Related papers  (2026-02-03T11:38:48Z)
• SWE-Universe: Scale Real-World Verifiable Environments to Millions [84.63665266236963]
  SWE-Universe is a framework for automatically constructing real-world software engineering (SWE) verifiable environments from GitHub pull requests (PRs)<n>We propose a building agent powered by an efficient custom-trained model to overcome the prevalent challenges of automatic building.<n>We demonstrate the profound value of our environments through large-scale agentic mid-training and reinforcement learning.
  arXiv  Detail & Related papers  (2026-02-02T17:20:30Z)
• MEnvAgent: Scalable Polyglot Environment Construction for Verifiable Software Engineering [54.236614097082395]
  We introduce MEnvAgent, a framework for automated Environment construction.<n>MEnvAgent employs a multi-agent Planning-Execution-Verification architecture to autonomously resolve construction failures.<n>MEnvData-SWE is the largest open-source polyglot dataset of realistic verifiable Docker environments to date.
  arXiv  Detail & Related papers  (2026-01-30T11:36:10Z)
• ASTRA: Automated Synthesis of agentic Trajectories and Reinforcement Arenas [13.919124676472022]
  ASTRA is an end-to-end framework for training tool-augmented language model agents.<n>ASTRA integrates scalable data synthesis and verifiable reinforcement learning.<n> Experiments on multiple agentic tool-use benchmarks demonstrate that ASTRA-trained models achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2026-01-29T11:22:23Z)
• Toward Training Superintelligent Software Agents through Self-Play SWE-RL [66.11447353341926]
  Self-play SWE-RL is a first step toward training paradigms for superintelligent software agents.<n>Our approach takes minimal data assumptions, only requiring access to sandboxed repositories with source code and installed dependencies.<n>Our results, albeit early, suggest a path where agents autonomously gather extensive learning experiences from real-world software repositories.
  arXiv  Detail & Related papers  (2025-12-21T00:49:40Z)
• PIPer: On-Device Environment Setup via Online Reinforcement Learning [74.52354321028493]
  Automated environment setup methods could assist developers by providing fully configured environments for arbitrary repositories without manual effort.<n>Recent studies reveal that even state-of-the-art Large Language Models (LLMs) achieve limited success in automating this task.<n>We combine supervised fine-tuning for generating correct scripts and Reinforcement Learning with Verifiable Rewards (RLVR) to adapt it to the task of environment setup.<n>On EnvBench-Python, our method enables Qwen3-8B (a model runnable on consumer hardware) to perform on par with larger models-Qwen3-32B and GPT-4
  arXiv  Detail & Related papers  (2025-09-29T20:03:05Z)
• SWE-rebench: An Automated Pipeline for Task Collection and Decontaminated Evaluation of Software Engineering Agents [31.921127664873882]
  LLM-based agents have shown promising capabilities in a growing range of software engineering (SWE) tasks.<n>High-quality training data is scarce, especially data that reflects real-world SWE scenarios.<n>Existing datasets are either limited to one-shot code generation or comprise small, manually curated collections of interactive tasks.
  arXiv  Detail & Related papers  (2025-05-26T18:01:00Z)
• Guided Search Strategies in Non-Serializable Environments with Applications to Software Engineering Agents [31.651748374218446]
  Large language models (LLMs) have recently achieved remarkable results in complex multi-step tasks.<n>They often struggle to maintain consistent performance across multiple solution attempts.
  arXiv  Detail & Related papers  (2025-05-19T18:50:15Z)
• R2E-Gym: Procedural Environments and Hybrid Verifiers for Scaling Open-Weights SWE Agents [32.06393076572057]
  AgentGym is the largest procedurally-curated executable gym environment for training real-world SWE-agents.<n>It is powered by two main contributions: SYNGEN, a synthetic data curation recipe, and Hybrid Test-time Scaling.<n>Overall, our approach achieves 51% on the SWE-Bench Verified benchmark, reflecting a new state-of-the-art for open-weight SWE-agents.
  arXiv  Detail & Related papers  (2025-04-09T17:55:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.