aiqbench analysis
SWE-Universe introduces a scalable framework that automatically constructs millions of real-world software engineering environments from GitHub pull requests to train coding agents. By utilizing an efficient building agent with iterative self-verification, the method overcomes traditional challenges like low production yield and high costs in generating verifiable tasks. This advancement provides a critical resource for advancing the next generation of agentic AI systems through large-scale reinforcement learning and mid-training.
SWE-Universe: Scale Real-World Verifiable Environments to Millions
arXiv:2602.02361v2 Announce Type: replace-cross Abstract: 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.
Abstract: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.
Software Engineering (cs.SE); Artificial Intelligence (cs.AI)
arXiv:2602.02361 [cs.SE]
(or arXiv:2602.02361v2 [cs.SE] for this version)
https://doi.org/10.48550/arXiv.2602.02361 Focus to learn more arXiv-issued DOI via DataCite
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