---
base_model:
- Qwen/Qwen3-8B
datasets:
- luzimu/webgen-agent_train_step-grpo
- luzimu/webgen-agent_train_sft
license: mit
pipeline_tag: image-text-to-text
library_name: transformers
---

# WebGen-Agent

WebGen-Agent is an advanced website generation agent designed to autonomously create websites from natural language instructions. It was introduced in the paper [WebGen-Agent: Enhancing Interactive Website Generation with Multi-Level Feedback and Step-Level Reinforcement Learning](https://arxiv.org/pdf/2509.22644v1).

Code: https://github.com/mnluzimu/WebGen-Agent

## Project Overview

WebGen-Agent combines state-of-the-art language models with specialized training techniques to create a powerful website generation tool. The agent can understand natural language instructions specifying appearance and functional requirements, iteratively generate website codebases, and refine them using visual and functional feedback.

## Resources

Links to the data and model parameters are as follows:

| **Data** | **HF Link** |
|----------|------|
| **webgen-agent_train_sft** | 🤗 [luzimu/webgen-agent_train_sft](https://huggingface.co/datasets/luzimu/webgen-agent_train_sft) |
| **webgen-agent_train_step-grpo** | 🤗 [luzimu/webgen-agent_train_step-grpo](https://huggingface.co/datasets/luzimu/webgen-agent_train_step-grpo) |

| **Model** | **HF Link** |
|----------|------|
| **WebGenAgent-LM-7B-SFT** | 🤗 [luzimu/WebGenAgent-LM-7B-SFT](https://huggingface.co/luzimu/WebGenAgent-LM-7B-SFT) |
| **WebGenAgent-LM-7B-Step-GRPO** | 🤗 [luzimu/WebGenAgent-LM-7B-Step-GRPO](https://huggingface.co/luzimu/WebGenAgent-LM-7B-Step-GRPO) |
| **WebGenAgent-LM-8B-SFT** | 🤗 [luzimu/WebGenAgent-LM-8B-SFT](https://huggingface.co/luzimu/WebGenAgent-LM-8B-SFT) |
| **WebGenAgent-LM-8B-Step-GRPO** | 🤗 [luzimu/WebGenAgent-LM-8B-Step-GRPO](https://huggingface.co/luzimu/WebGenAgent-LM-8B-Step-GRPO) |

## How WebGen-Agent Works

WebGen-Agent follows an iterative, multi-step paradigm for website generation:

1.  **Code Generation**: The agent generates code to create or edit website files based on natural language instructions
2.  **Code Execution**: Dependencies are installed and the website service is started
3.  **Feedback Gathering**: 
   - A screenshot of the website is captured
   - A Visual Language Model (VLM) provides appearance feedback and scores
   - A GUI-agent tests the website functionality and provides functional feedback
4.  **Refinement**: Based on the feedback, the agent continues to improve the website until it meets requirements

![WebGen-Agent Workflow](https://github.com/mnluzimu/WebGen-Agent/raw/main/paper/webgen-agent.png)

## Step-GRPO with Screenshot and GUI-agent Feedback

The Step-GRPO with Screenshot and GUI-agent Feedback approach uses the screenshot and GUI-agent scores inherently produced in the WebGen-Agent workflow as step-level rewards:
-   **Screenshot Score**: Quantifies the visual appeal and aesthetics of the website
-   **GUI-agent Score**: Measures how well the website meets functional requirements

These dual rewards provide dense, reliable process supervision that significantly improves the model's ability to generate high-quality websites.

![Step-GRPO with Screenshot and GUI-agent Feedback](https://github.com/mnluzimu/WebGen-Agent/raw/main/paper/step-grpo.png)

## Citation

If you find our project useful, please cite:

```
@misc{lu2025webgenagentenhancinginteractivewebsite,
      title={WebGen-Agent: Enhancing Interactive Website Generation with Multi-Level Feedback and Step-Level Reinforcement Learning}, 
      author={Zimu Lu and Houxing Ren and Yunqiao Yang and Ke Wang and Zhuofan Zong and Junting Pan and Mingjie Zhan and Hongsheng Li},
      year={2025},
      eprint={2509.22644},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2509.22644}, 
}

@misc{lu2025webgenbenchevaluatingllmsgenerating,
      title={WebGen-Bench: Evaluating LLMs on Generating Interactive and Functional Websites from Scratch}, 
      author={Zimu Lu and Yunqiao Yang and Houxing Ren and Haotian Hou and Han Xiao and Ke Wang and Weikang Shi and Aojun Zhou and Mingjie Zhan and Hongsheng Li},
      year={2025},
      eprint={2505.03733},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2505.03733}, 
}
```