Qwen today released Qwen-AgentWorld, billed as the first native Language World Model (LWM) that can simulate agent interaction environments across seven domains. Native world modeling: environment modeling is an explicit training objective beginning in continued pretraining (CPT) and maintained through CPT → supervised fine-tuning (SFT) → reinforcement learning (RL), rather than as a post‑hoc adaptation of a general LLM. Seven domains, one model: a single model covers text environments (MCP, Sea

Qwen today released Qwen-AgentWorld, billed as the first native Language World Model (LWM) that can simulate agent interaction environments across seven domains. Native world modeling: environment modeling is an explicit training objective beginning in continued pretraining (CPT) and maintained through CPT → supervised fine-tuning (SFT) → reinforcement learning (RL), rather than as a post‑hoc adaptation of a general LLM. Seven domains, one model: a single model covers text environments (MCP, Search, Terminal, SWE) and GUI environments (Web, OS, Android), enabling cross‑domain knowledge transfer.