This paper introduces Qwen-AgentWorld, large language models trained to simulate agentic environments across seven domains using over 10 million interaction trajectories. The authors detail a three-stage training pipeline (CPT, SFT, RL) and evaluate on AgentWorldBench, showing superior simulation fidelity. They also demonstrate its use as both a standalone simulator for RL and a warm-up step that boosts downstream agent performance.

This essay breaks down the term “world model” by tracing it to the POMDP perception-action loop and identifying its key components—renderers, simulators, planners, and their connecting loop. It shows how different AI fields project parts of this loop to build spatial and temporal understanding beyond language models.

The article unpacks the long history of “world models,” from Schmidhuber’s 1990 proposal through Sutton’s Dyna and Ha & Schmidhuber’s 2018 implementation, to LeCun’s JEPA framework and the new LeWorldModel paper. It explains LeCun’s real contribution—predicting in latent space with SIGReg to prevent collapse—while calling out overhyped funding claims and noting limitations and prior art.