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---
license_link: https://www.apache.org/licenses/LICENSE-2.0
license: apache-2.0
language:
- en
- zh
pipeline_tag: text-generation
library_name: transformers
tags:
- WebWorld
- web-agent
- world-model
- simulator
- browser
- a11y
- html
- xml
- markdown
- long-horizon
- long-context
- synthetic-trajectories
- instruction-tuning
base_model_relation: finetune
base_model:
- Qwen/Qwen3-8B
datasets:
- Qwen/WebWorldData
---
# WebWorld 🌐
[![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)
## 📚 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](https://huggingface.co/Qwen/Qwen3-8B) | [🤗 HuggingFace](https://huggingface.co/Qwen/WebWorld-8B) | [🤖 ModelScope](https://modelscope.cn/models/Qwen/WebWorld-8B) |
| **WebWorld-14B** | [Qwen3-14B](https://huggingface.co/Qwen/Qwen3-14B) | [🤗 HuggingFace](https://huggingface.co/Qwen/WebWorld-14B) | [🤖 ModelScope](https://modelscope.cn/models/Qwen/WebWorld-14B) |
| **WebWorld-32B** | [Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B) | [🤗 HuggingFace](https://huggingface.co/Qwen/WebWorld-32B) | [🤖 ModelScope](https://modelscope.cn/models/Qwen/WebWorld-32B) |
**WebWorldData**: [Huggingface: Qwen/WebWorldData](https://huggingface.co/datasets/Qwen/WebWorldData), [ModelScope: Qwen/WebWorldData](https://modelscope.cn/datasets/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
<details>
<summary>💻 Click to expand code</summary>
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "Qwen/WebWorld-8B" # or WebWorld-14B, WebWorld-32B
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_name,
device_map="auto",
torch_dtype=torch.bfloat16,
trust_remote_code=True,
).eval()
system_prompt = (
"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.\n"
"Strictly maintain the original format. Output only the full page state "
"without explanations, code, or truncation."
)
current_state = """RootWebArea 'Global Start - Your Daily Portal', focused
\t[1] banner 'Top Header', visible
\t\t[2] link 'Set as Homepage', clickable, visible
\t\t[3] link 'Feedback', clickable, visible
\t\t[5] region 'Weather Widget', visible
\t\t\tStaticText 'New York, USA'
\t\t\t[6] image 'Sunny', visible
\t\t\tStaticText '24°C'
\t\t[8] link 'Sign In', clickable, visible
\t[10] region 'Search Area', visible
\t\t[11] image 'Global Start Logo', visible
\t\tStaticText 'Search the entire web'
\t\t[12] tablist 'Search Engine Selector', orientation='horizontal'
\t\t\t[13] tab 'Google', selected=True, clickable
\t\t\t[14] tab 'Bing', selected=False, clickable
\t\t\t[15] tab 'DuckDuckGo', selected=False, clickable
\t\t[18] combobox 'Web Search', clickable, visible, autocomplete='both', expanded=False
\t\t\t[19] textbox 'Type keywords or URL...', clickable, visible, editable, value=''
\t\t[20] button 'Search', clickable, visible
\t[30] navigation 'Category Bar', visible
\t\t[31] link 'Home', clickable, selected=True
\t\t[32] link 'News', clickable
\t\t[33] link 'Video', clickable
\t\t[34] link 'Shopping', clickable
\t\t[35] link 'Social', clickable
\t[50] main 'Site Directory', visible
\t\t[51] region 'Top Recommended', visible
\t\t\t[52] heading 'Most Popular', visible
\t\t\t[53] list 'Top Sites Grid', visible
\t\t\t\t[54] link 'Facebook', clickable
\t\t\t\t[56] link 'YouTube', clickable
\t\t\t\t[58] link 'Amazon', clickable
\t\t\t\t[60] link 'Twitter / X', clickable
\t\t\t\t[62] link 'Instagram', clickable
\t\t\t\t[64] link 'Wikipedia', clickable
\t\t\t\t[66] link 'Netflix', clickable
\t\t\t\t[68] link 'LinkedIn', clickable
\t\t[80] region 'News & Media', visible
\t\t\t[81] heading 'Latest News', visible
\t\t\t[82] link 'CNN', clickable
\t\t\t[83] link 'BBC', clickable
\t\t\t[84] link 'The Verge', clickable
\t\t[90] region 'Shopping', visible
\t\t\t[91] heading 'E-Commerce', visible
\t\t\t[92] link 'eBay', clickable
\t\t\t[93] link 'Walmart', clickable
\t\t\t[94] link 'Best Buy', clickable
\t[200] complementary 'Ads', visible
\t\t[201] image 'Ad: Travel to Japan'
\t\t[202] link 'Book Now', clickable
\t[300] contentinfo 'Footer', visible
\t\tStaticText '© 2026 Global Start Inc.'"""
user_message = (
f"Initial Page State:\n{current_state}\n\n"
f"First Action: 'click([32])'\n\n"
f"Next Page State:"
)
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_message},
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=4096,
do_sample=False,
)
response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)
print(response)
```
</details>
### Multi-Turn Simulation
The first turn provides the initial state and first action. Each subsequent turn uses a fixed continuation prompt:
<details>
<summary>💻 Click to expand code</summary>
```python
CONTINUE_PROMPT = (
"Continue the trajectory. Given the previous state, "
"predict the next page state after this action.\n\n"
"Action: '{action}'\n\nNext Page State:"
)
# Turn 1
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": f"Initial Page State:\n{state_0}\n\nFirst Action: '{action_0}'\n\nNext Page State:"},
]
state_1 = generate(messages) # your generate function
# Turn 2
messages.append({"role": "assistant", "content": state_1})
messages.append({"role": "user", "content": CONTINUE_PROMPT.format(action=action_1)})
state_2 = generate(messages)
# Turn 3, 4, ... up to 30+ turns: repeat the same pattern
messages.append({"role": "assistant", "content": state_2})
messages.append({"role": "user", "content": CONTINUE_PROMPT.format(action=action_2)})
state_3 = generate(messages)
```
</details>
## 🎮 Action Space
WebWorld supports a unified action space as Python-style function calls:
| Category | Action | Description |
|---|---|---|
| **Element** | `click(bid, button, modifiers)` | Click a DOM element by its ID |
| | `fill(bid, text, press_enter)` | Type text into an input field |
| | `select_option(bid, options)` | Select from a dropdown / combobox |
| | `hover(bid)` | Hover over an element |
| **Mouse** | `mouse_move(x, y)` | Move cursor to coordinates |
| | `mouse_click(x, y, button)` | Click at coordinates |
| | `mouse_down(x, y)` / `mouse_up(x, y)` | Press / release (drag-and-drop) |
| **Keyboard** | `keyboard_press(key)` | Press a key (e.g., `Enter`, `Tab`) |
| | `keyboard_type(text)` | Type a string sequentially |
| **Browser** | `scroll(dx, dy)` | Scroll the viewport |
| | `goto(url)` | Navigate to a URL |
| | `go_back()` / `go_forward()` | Browser history navigation |
| | `tab_new()` / `tab_close()` / `tab_focus(index)` | Manage browser tabs |
| **Meta** | `send_msg_to_user(text)` | Send a message to the user |
| | `noop(wait_ms)` | Wait for a duration |
| | `infeasible(reason)` | Declare the task impossible |
## 📊 Performance
### Intrinsic Evaluation (WebWorld-Bench)
WebWorld-Bench evaluates models using **Factuality Score** (functional correctness) and **Web Turing Score** (perceptual realism) across nine dimensions:
| Model | Avg Factuality | Avg Turing |
|---|---|---|
| GPT-4o | 59.5 | 35.4 |
| Claude-Opus-4.1 | **71.3** | **47.4** |
| Gemini-3-Pro | 70.3 | 43.2 |
| Qwen3-8B (base) | 26.9 | 17.4 |
| **WebWorld-8B** | **70.1** | **42.2** |
| **WebWorld-14B** | 70.7 | 44.7 |
| **WebWorld-32B** | **71.0** | **45.6** |
### Extrinsic Evaluation (Agent Training)
| Model | MiniWob++ SR | WebArena SR |
|---|---|---|
| GPT-4o | 64.3% | 26.6% |
| Qwen3-8B (base) | 49.4% | 9.8% |
| **Qwen3-8B + WebWorld** | **59.3%** (+9.9%) | **20.7%** (+10.9%) |
| Qwen3-14B (base) | 54.9% | 15.1% |
| **Qwen3-14B + WebWorld** | **63.2%** (+8.3%) | **24.3%** (+9.2%) |
### Cross-Domain Generalization
| Environment | Qwen3-8B | WebWorld-8B | Gain |
|---|---|---|---|
| API Services | 0.088 | **0.299** | +0.211 |
| Code | 0.147 | **0.396** | +0.249 |
| Game | 0.253 | **0.473** | +0.220 |
| GUI Desktop | 0.322 | **0.705** | +0.383 |
## ⚠️ Limitations
- **Sycophancy / optimism bias**: the model may generate outcomes that are overly favorable to the agent's intended action.
- **Content generation fidelity**: long-form, high-precision content (e.g., scientific articles) is not the primary target.
- **Text-only**: WebWorld does not simulate visual / pixel-level rendering.
## 📝 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},
}

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{
"architectures": [
"Qwen3ForCausalLM"
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"content": "<|fim_middle|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151661": {
"content": "<|fim_suffix|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151662": {
"content": "<|fim_pad|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151663": {
"content": "<|repo_name|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151664": {
"content": "<|file_sep|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151665": {
"content": "<tool_response>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151666": {
"content": "</tool_response>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151667": {
"content": "<think>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151668": {
"content": "</think>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
}
},
"additional_special_tokens": [
"<|im_start|>",
"<|im_end|>",
"<|object_ref_start|>",
"<|object_ref_end|>",
"<|box_start|>",
"<|box_end|>",
"<|quad_start|>",
"<|quad_end|>",
"<|vision_start|>",
"<|vision_end|>",
"<|vision_pad|>",
"<|image_pad|>",
"<|video_pad|>"
],
"bos_token": null,
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set content = message.content %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is defined and message.reasoning_content is not none %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '</think>' in message.content %}\n {%- set content = message.content.split('</think>')[-1].lstrip('\\n') %}\n {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '<think>\\n\\n</think>\\n\\n' }}\n {%- endif %}\n{%- endif %}",
"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"errors": "replace",
"extra_special_tokens": {},
"model_max_length": 131072,
"pad_token": "<|endoftext|>",
"padding_side": "right",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null
}

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