Qwen/WebWorld-32B
Qwen/WebWorld-32B is a 32 billion parameter open-web world model from Qwen, designed for training and evaluating web agents. It is trained on over 1 million real-world web interaction trajectories, supporting long-horizon simulation and multi-format state representations like A11y Tree, HTML, and Markdown. This model excels at predicting next page states and enabling CoT-activated reasoning for transition prediction, demonstrating superior performance in web agent tasks compared to base models and even outperforming GPT-5 as a world model in inference-time lookahead search.
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WebWorld-32B: A World Model for Web Agents
Qwen/WebWorld-32B is a 32 billion parameter model from the WebWorld series, specifically designed as an open-web world model for training and evaluating web agents. It is built upon the Qwen3-32B base model and has been extensively trained on over 1 million real-world web interaction trajectories using a scalable hierarchical data pipeline.
Key Capabilities & Features
- Long-horizon simulation: Supports web interaction simulations spanning 30+ steps.
- Multi-format state representations: Handles various web state formats including A11y Tree, HTML, XML, Markdown, and natural language.
- CoT-activated reasoning: Incorporates Chain-of-Thought reasoning for accurate transition prediction between web states.
- Cross-domain generalization: Demonstrates strong performance across diverse environments such as code, GUI, and game environments.
- Enhanced agent performance: Agents trained with WebWorld-synthesized trajectories show significant improvements, achieving +9.9% on MiniWob++ and +10.9% on WebArena compared to base models.
- Superior world modeling: Outperforms GPT-5 as a world model during inference-time lookahead search.
Performance Highlights
WebWorld-32B achieves high scores in intrinsic evaluations, with a Factuality Score of 71.0 and a Web Turing Score of 45.6, indicating strong functional correctness and perceptual realism. In extrinsic evaluations, it significantly boosts the success rate of web agents.
Ideal Use Cases
- Developing and training web agents: Provides a robust environment for simulating web interactions.
- High-fidelity web simulation: Recommended for scenarios requiring accurate and long-horizon web state prediction.
- Task-specific fine-tuning: Can be further fine-tuned on in-domain trajectories for optimal results in specific web environments.