SWE-Universe：將可驗證的真實世界環境擴展至百萬規模

SWE-Universe: Scale Real-World Verifiable Environments to Millions

February 2, 2026

作者: Mouxiang Chen, Lei Zhang, Yunlong Feng, Xuwu Wang, Wenting Zhao, Ruisheng Cao, Jiaxi Yang, Jiawei Chen, Mingze Li, Zeyao Ma, Hao Ge, Zongmeng Zhang, Zeyu Cui, Dayiheng Liu, Jingren Zhou, Jianling Sun, Junyang Lin, Binyuan Hui

cs.AI

摘要

我們提出SWE-Universe，這是一個可擴展且高效的框架，能從GitHub拉取請求（PR）自動構建真實世界的軟體工程（SWE）可驗證環境。為克服自動構建中普遍存在的挑戰（如產出率低、驗證機制薄弱及成本過高），本框架採用由高效自訂訓練模型驅動的構建代理。該代理通過迭代式自我驗證與循環內駭客檢測技術，確保可靠生成高保真度的可驗證任務。運用此方法，我們將真實世界多語言SWE環境的規模擴展至百萬級（807,693個）。我們通過大規模代理中期訓練與強化學習，證明了這些環境的重要價值。最終，我們將此技術應用於Qwen3-Max-Thinking模型，並在SWE-Bench Verified上獲得75.3%的評分。本研究為推進下一代編碼代理的發展，提供了關鍵資源與穩健的方法論。

English

We propose SWE-Universe, a scalable and efficient framework for automatically constructing real-world software engineering (SWE) verifiable environments from GitHub pull requests (PRs). To overcome the prevalent challenges of automatic building, such as low production yield, weak verifiers, and prohibitive cost, our framework utilizes a building agent powered by an efficient custom-trained model. This agent employs iterative self-verification and in-loop hacking detection to ensure the reliable generation of high-fidelity, verifiable tasks. Using this method, we scale the number of real-world multilingual SWE environments to a million scale (807,693). We demonstrate the profound value of our environments through large-scale agentic mid-training and reinforcement learning. Finally, we applied this technique to Qwen3-Max-Thinking and achieved a score of 75.3% on SWE-Bench Verified. Our work provides both a critical resource and a robust methodology to advance the next generation of coding agents.