Qwen-AgentWorld trains language models on over 10 million interaction trajectories as an environment simulator to train AI agents through virtual environments and improve their performance across seven benchmarks.
Qwen-AgentWorld leverages language models as learned environment simulations to efficiently train autonomous agents and improve their reasoning through chain-of-thought prompting.
Visual world models can be systematically manipulated through visually imperceptible image modifications to generate erroneous predictions without requiring knowledge of future data or user inputs.
