WebWorld-32B

WebWorld 🌐 📚 Introduction WebWorld** is a large-scale **open-web world model** series for training and evaluating web agents. It is trained on **1M+ real-world web interaction trajectories** via a scalable hierarchical data pipeline, supporting: **Long-horizon simulation** (30+ steps) **Multi-format state representations**: A11y Tree, HTML, XML, Markdown, and natural language **CoT-activated reasoning** for transition prediction **Cross-domain generalization** to code, GUI, and game environments Agents trained on WebWorld-synthesized trajectories achieve **+9.9% on MiniWob++** and **+10.9% on WebArena**. When used for inference-time lookahead search, WebWorld **outperforms GPT-5** as a world model. 🎯 Model Series | Model | Base Model | HuggingFace Link | ModelScope Link | |---|---|---|---| | **WebWorld-8B** | Qwen3-8B | 🤗 HuggingFace | 🤖 ModelScope | | **WebWorld-14B** | Qwen3-14B | 🤗 HuggingFace | 🤖 ModelScope | | **WebWorld-32B** | Qwen3-32B | 🤗 HuggingFace | 🤖 ModelScope | WebWorldData**: Huggingface: Qwen/WebWorldData, ModelScope: Qwen/WebWorldData 💡 **Recommendation**: Use 8B for fast simulation and data synthesis; use 14B/32B for higher-fidelity simulation and better long-horizon robustness. For best results in a specific environment, we recommend task-specific fine-tuning on in-domain trajectories. 🛠️ Requirements `transformers` (recommended: latest version) `torch` Optional: `accelerate`, `vllm` for efficient serving 🚀 Quick Start Key Notes:** WebWorld predicts the next page state given the current state and an action. It strictly preserves the input/output format (A11y / HTML / XML / Markdown / NL). Supports multi-turn trajectory simulation up to 30+ steps. Single-Step Prediction 💻 Click to expand code Multi-Turn Simulation The first turn provides the initial state and first action. Each subsequent turn uses a fixed continuation prompt: 💻 Click to expand code 🎮 Action Space WebWorld supports a unified action space as Python-style function calls: | Category