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Qwen-AgentWorld: An open world model for AI agents
Alibaba’s Qwen team has open sourced their 35B parameter AgentWorld simulator model, which learns to mimic the agent’s working environments such as web browsers, command line interfaces, file systems, etc. It’s trained on 10 million real interaction trajectories and is the first openly available world simulator model, with world simulation as its primary training goal.
Qwen-AgentWorld is a Mixture-of-Experts language model with 35 billion parameters (only 3 billion active at a given time). It’s trained to imitate the working environment of AI agents such as web browsers, CLI, OS, mobile apps, search engines, dev tools, etc. This model is built by Alibaba’s Qwen team with over 10 million real-world interaction trajectories through a 3-stage training pipeline: continued pre-training, supervised fine-tuning, and reinforcement learning. With a 256K token context length, it can be easily used with different inference engines such as SGLang, vLLM, and Transformers, so that it outperforms almost all proprietary models on AgentWorldBench.
Why a vibe-coder should care
With powerful world models, your AI code assistants (e.g., Claude Code) could help agents to safely operate apps/browsers/filesystems without any hesitation or hallucinations. That’s how we build useful AI agents other than just demoing them.
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