Alibaba introduces Qwen-AgentWorld: a model for simulating complex environments

Alibaba has introduced Qwen-AgentWorld—a new model that serves as a 'world model' in natural language. This model is capable of simulating seven different environments, including the terminal, web browser, operating systems (OS), and Android, all within a single architecture. Unlike previous approaches where environment simulation was merely a random side effect of training, Qwen-AgentWorld was trained specifically for environment modeling, which significantly increases its efficiency. Developers claim that this model expands the capabilities of agents, allowing them not only to act in real environments but also to effectively simulate them within the LLM itself. In two key areas, Qwen demonstrates superiority: firstly, it creates a foundational world simulation model that, according to claims, surpasses other models like Claude Opus 4.8 and GPT-5.4 on the AgentWorldBench benchmark. Secondly, it shows that the ability to model the world improves agent training, even without specialized reinforcement learning (RL), because the predictive knowledge gained from simulation easily transfers to complex agent tasks.