WebWorldData_copy

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+---
+license: apache-2.0
+task_categories:
+- text-generation
+language:
+- en
+- zh
+tags:
+- WebWorld
+- world-model
+- web-agent
+- browser-simulation
+- a11y
+- html
+- xml
+- markdown
+- trajectories
+- agent-training
+- synthetic-data
+pretty_name: WebWorldData
+size_categories:
+- 1M<n<10M
+---
+# WebWorldData 🌐
+[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/LICENSE-2.0)
+[![GitHub](https://img.shields.io/badge/GitHub-WebWorld-4b32c3?logo=github)](https://github.com/QwenLM/WebWorld)
+[![Dataset](https://img.shields.io/badge/HF%20Dataset-WebWorldData-yellow?logo=huggingface)](https://huggingface.co/datasets/Qwen/WebWorldData)
+[![MS Dataset](https://img.shields.io/badge/ModelScope-Dataset-7B42BC)](https://modelscope.cn/datasets/Qwen/WebWorldData)
+[![8B](https://img.shields.io/badge/Model-8B-green?logo=huggingface)](https://huggingface.co/Qwen/WebWorld-8B)
+[![MS 8B](https://img.shields.io/badge/ModelScope-8B-7B42BC)](https://modelscope.cn/models/Qwen/WebWorld-8B)
+[![14B](https://img.shields.io/badge/Model-14B-green?logo=huggingface)](https://huggingface.co/Qwen/WebWorld-14B)
+[![MS 14B](https://img.shields.io/badge/ModelScope-14B-7B42BC)](https://modelscope.cn/models/Qwen/WebWorld-14B)
+[![32B](https://img.shields.io/badge/Model-32B-green?logo=huggingface)](https://huggingface.co/Qwen/WebWorld-32B)
+[![MS 32B](https://img.shields.io/badge/ModelScope-32B-7B42BC)](https://modelscope.cn/models/Qwen/WebWorld-32B)
+## Overview
+**WebWorldData** is a large-scale dataset of **1.06M web interaction trajectories** collected from the open web, designed for training browser world models. It is the training data behind the [WebWorld](https://github.com/QwenLM/WebWorld) model series.
+Each trajectory consists of sequences of `(state, action, next_state)` transitions, where states are represented as A11y Trees extracted from real websites using Playwright.
+## Dataset Statistics
+| | |
+|---|---|
+| **Total Trajectories** | 1,059,348 |
+| **Total Size** | ~50.9 GB |
+| **Languages** | English, Chinese |
+| **Max Context Length** | 30K tokens |
+| **Max Trajectory Turns** | 30+ steps |
+| **State Format** | A11y Tree (primary), HTML, XML, Markdown, Natural Language |
+| **Source Websites** | 680K+ URLs from FineWeb, CCI 3.0, and curated lists |
+## Data Sources
+The dataset is collected through a **scalable hierarchical pipeline**:
+| Source | Strategy | Scale | Description |
+|---|---|---|---|
+| **Level 1: Randomized Crawling** | Rule-based crawlers | 293K | Randomized exploration on websites from pre-training corpora (FineWeb, CCI 3.0), aligned with the base model's linguistic priors |
+| **Level 2: Autonomous Exploration** | LLM-driven agents | 38K | Agents autonomously explore websites by generating their own objectives, producing long-horizon trajectories up to 30 steps |
+| **Level 3: Task-Oriented Execution** | Synthetic tasks | 94K | Agents execute synthesized web tasks through seed extraction, diversification, and paraphrasing |
+| **Open Source** | AgentTrek, etc. | 38K | Reformatted open-source agent trajectories |
+| **Multi-Format** | Format conversion | 48K | Trajectories converted to HTML, XML, Markdown, Playwright formats |
+| **Interaction** | General QA + chat | 548K | General instruction-following and QA data to preserve conversational abilities |
+## Data Format
+Each sample is a multi-turn conversation in JSONL format:
+```json
+{
+  "messages": [
+    {
+      "role": "system",
+      "content": "You are a web world model. I will provide you with an initial page state and a sequence of actions. For each action, predict the resulting page state.\nStrictly maintain the original format. Output only the full page state without explanations, code, or truncation."
+    },
+    {
+      "role": "user",
+      "content": "Initial Page State:\nRootWebArea 'Example Site'\n\t[1] banner ...\n\nFirst Action: 'click([32])'\n\nNext Page State:"
+    },
+    {
+      "role": "assistant",
+      "content": "RootWebArea 'Example Site - News'\n\t[1] banner ...\n\t[50] main ..."
+    },
+    {
+      "role": "user",
+      "content": "Continue the trajectory. Given the previous state, predict the next page state after this action.\n\nAction: 'fill([19], \"weather today\")'\n\nNext Page State:"
+    },
+    {
+      "role": "assistant",
+      "content": "RootWebArea 'Example Site - News'\n\t[1] banner ...\n\t[19] textbox ..., value='weather today' ..."
+    }
+  ]
+}
+```
+## Domain Distribution
+The dataset covers diverse web domains:
+| Domain | Share |
+|---|---|
+| Technology | 15.2% |
+| E-Commerce / Shopping | 13.8% |
+| News & Media | 12.1% |
+| Education | 10.5% |
+| Entertainment | 9.3% |
+| Lifestyle | 8.7% |
+| Business & Finance | 7.9% |
+| Government & Public Services | 6.4% |
+| Health | 5.8% |
+| Other | 10.3% |
+## Action Space
+Trajectories use a unified action space as Python-style function calls:
+| Category | Actions |
+|---|---|
+| **Element** | `click`, `fill`, `select_option`, `hover` |
+| **Mouse** | `mouse_move`, `mouse_click`, `mouse_down`, `mouse_up` |
+| **Keyboard** | `keyboard_press`, `keyboard_type` |
+| **Browser** | `scroll`, `goto`, `go_back`, `go_forward`, `tab_new`, `tab_close`, `tab_focus` |
+| **Meta** | `send_msg_to_user`, `noop`, `infeasible` |
+Action distribution: Element interactions (83.4%), Browser & navigation (11.9%), Meta & control (2.4%), Keyboard (1.2%), Coordinate & mouse (1.1%).
+## Filtering & Safety
+The dataset undergoes rigorous dual-stage filtering:
+1. **Rule-based filtering**: Website reachability checks, banned keyword filtering (pornography, gambling, violence), trajectory pruning for no-op transitions
+2. **LLM-based URL filtering**: Each URL scored across accessibility, content suitability, interactivity, and engineering quality
+3. **Trajectory-level filtering**: Max 30K tokens, max 30 turns, keyword safety checks
+All data is collected from publicly accessible webpages in compliance with `robots.txt` protocols.
+## Usage
+```python
+from datasets import load_dataset
+dataset = load_dataset("Qwen/WebWorldData")
+```
+## Intended Use
+- Training browser world models for web simulation
+- Generating synthetic trajectories for web agent fine-tuning
+- Research on world modeling, environment simulation, and agent learning
+## Limitations
+- Data is collected from publicly accessible webpages; residual PII may exist despite filtering
+- Web content is inherently non-deterministic (ads, A/B tests, dynamic widgets) — some trajectories may not be perfectly reproducible
+- Domain distribution reflects the composition of FineWeb and CCI 3.0 pre-training corpora
+## Associated Models
+| Model | Link |
+|---|---|
+| WebWorld-8B | [🤗 HuggingFace](https://huggingface.co/Qwen/WebWorld-8B) |
+| WebWorld-14B | [🤗 HuggingFace](https://huggingface.co/Qwen/WebWorld-14B) |
+| WebWorld-32B | [🤗 HuggingFace](https://huggingface.co/Qwen/WebWorld-32B) |
+## Citation
+```bibtex
+@misc{xiao2026webworldlargescaleworldmodel,
+      title={WebWorld: A Large-Scale World Model for Web Agent Training},
+      author={Zikai Xiao and Jianhong Tu and Chuhang Zou and Yuxin Zuo and Zhi Li and Peng Wang and Bowen Yu and Fei Huang and Junyang Lin and Zuozhu Liu},
+      year={2026},
+      eprint={2602.14721},
+      archivePrefix={arXiv},
+      primaryClass={cs.AI},
+      url={https://arxiv.org/abs/2602.14721},
+}
+```
+## License
+This dataset is released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0).

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