Qwen-AgentWorld: Language World Models for General Agents

We discuss Qwen-AgentWorld, a pioneering suite of language world models designed to simulate complex digital environments for artificial intelligence agents. By training on over 10 million trajectories across seven domains, including operating systems, web browsers, and software engineering sandboxes, these models learn to predict how an environment will respond to specific actions. This simulation capability allows agents to rehearse scenarios, refine their decision-making, and learn from a vast scale of diverse interactions without needing constant access to live, physical systems. The research details a three-stage training pipeline consisting of continual pre-training, supervised fine-tuning, and reinforcement learning to ensure high fidelity in these virtual environments. Furthermore, the paper presents AgentWorldBench, a rigorous new benchmark used to verify that these world models can accurately mimic real-world dynamics. Ultimately, the authors demonstrate that integrating world modeling into agent frameworks significantly boosts performance by providing a foundation for predictive reasoning and planning.