RecreationWorld (QwenLM): Scalable Verifiable Environments for Hybrid Computer-Use Agents
Qwen's new open repo: scalable and verifiable environments for training and evaluating hybrid computer-use agents.
Why it matters
A fresh environment harness from the Qwen team for computer-use agent training; verifiable environments are the bottleneck for RL on real computer tasks.
What you could build with it
Use the verifiable environments as the training and eval sandbox for fine-tuning computer-use agents with real pass/fail signals.
Does it hold up?
Too early to judge: repo is 6 days old with no third-party coverage yet; it ships from the Qwen team, so quality is likely solid once people try it.
Built with RecreationWorld (QwenLM)
- RecreationWorld paper — arXiv 2609.22000article · Qwen team paper: 250-task RecreationBench across Ubuntu/macOS/Windows/Android/Web; GPT-6 Astra leads at 58.1% but passes all programmatic tests on only 2.8% of tasks.
- RecreationWorld paper summary — TrendToKnowarticle · Third-party summary of the paper: five-platform recreation tasks, transfer gains on OSWorld 2.0, Vision2Web and WeaveBench, and a builder takeaway that hidden action-conditioned tests are essential.
Learn more
First spotted on github: source.
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