📣📣 Meet Qwen-AgentWorld - a native language world model that simulates 7 agent environments （MCP， Search， Terminal， SWE， Web， OS， Android） within a single model. Environment modeling is the training objective from day one， not a post-hoc adaptation.

🤔 LLMs are trained to be better agents - better at acting in environments. But nobody has trained them to model the environments themselves.

🗺️ Our roadmap： investigate how language world modeling can push the boundaries of general agent capabilities， along two routes：

1️⃣ Build a foundation model for environment simulation - outperforming Claude Opus 4.8 and GPT-5.4 on AgentWorldBench

2️⃣ Investigate how world modeling enhances agent training： 🔬 Controllable Sim RL （agentic RL with LWM as environments） surpasses training in real environments 🧠 Learning to predict environments （LWM warm-up） makes agents stronger - remarkably， even without any agent-specific training， this predictive knowledge transfers to agentic tasks with zero fine-tuning

🔗 Model Studio： https：//int.alibabacloud.com/m/1000413253/