Qwen-AgentWorld Trains a Language Model as a World Model for RL Agents: World Model as a Decoupled RL Simulator

Short summary

Qwen-AgentWorld trains a language model to predict environment state changes, enabling RL agents to train in parallel simulation instead of slow real-time interaction. Like flight simulators for pilots, it decouples training from the live environment through learned world models. The approach combines continual pre-training, supervised fine-tuning, and RL optimization, then serves as a foundation model for downstream agent development.

• •Language model trained as world model predicts next environment state from observations and actions
• •Enables parallel training of RL agents in simulation instead of sequential real-world interaction
• •Three-stage training pipeline produces foundation model for downstream agent fine-tuning

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