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Qwen
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Model Details
Model Provider
Qwen
Model Tree
Qwen/Qwen3-8B
Input Modalities
Output Modalities
Supported Functionality
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:
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 | 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.
transformers (recommended: latest version)torchaccelerate, vllm for efficient servingKey Notes:
python
import torchfrom transformers import AutoTokenizer, AutoModelForCausalLM model_name = "Qwen/WebWorld-8B" # or WebWorld-14B, WebWorld-32Btokenizer = 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)The first turn provides the initial state and first action. Each subsequent turn uses a fixed continuation prompt:
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 1messages = [ {"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 2messages.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 patternmessages.append({"role": "assistant", "content": state_2})messages.append({"role": "user", "content": CONTINUE_PROMPT.format(action=action_2)})state_3 = generate(messages)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 |
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 |
| 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%) |
| 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 |
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}, }| 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 |
| 70.7 |
| 44.7 |
| WebWorld-32B | 71.0 | 45.6 |