| --- |
| license: apache-2.0 |
| task_categories: |
| - image-to-text |
| language: |
| - en |
| - zh |
| tags: |
| - agent |
| - code |
| size_categories: |
| - 100K<n<1M |
| --- |
| |
| <p align="center"> |
| <img src="./docs/assets/logo.svg" alt="Logo" width="120" /> |
| <p align="center"> |
| <a href="https://github.com/PKU-DAIR"> |
| <img alt="Static Badge" src="https://img.shields.io/badge/%C2%A9-PKU--DAIR-%230e529d?labelColor=%23003985"> |
| </a> |
| </p> |
| </p> |
| |
| ## **WebRenderBench: Enhancing Web Interface Generation through Layout-Style Consistency and Reinforcement Learning** |
|
|
| [Paper](https://arxiv.org/pdf/2510.04097) | [中文](./docs/Chinese.md) |
|
|
| ## **🔍 Overview** |
|
|
| **WebRenderBench** is a large-scale benchmark designed to advance **WebUI-to-Code** research for multimodal large language models (MLLMs) through evaluation on real-world webpages. It provides: |
|
|
| * **45,100** real webpages collected from public portal websites |
| * **High diversity and complexity**, covering a wide range of industries and design styles |
| * **Novel evaluation metrics** that quantify **layout and style consistency** based on rendered pages |
| * The **ALISA reinforcement learning framework**, which uses the new metrics as reward signals to optimize generation quality |
|
|
| --- |
|
|
| ## **🚀 Key Features** |
|
|
| ### **Beyond the Limitations of Traditional Benchmarks** |
|
|
| WebRenderBench addresses the core issues of existing WebUI-to-Code benchmarks in data quality and evaluation methodology: |
|
|
| | Aspect | Traditional Benchmarks | Advantages of WebRenderBench | |
| | :------------------------- | :---------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------- | |
| | **Data Quality** | Small-scale, simple-structured, or LLM-synthesized data with limited diversity | Large-scale, real-world, and structurally complex webpages that present higher challenges | |
| | **Evaluation Reliability** | Relies on visual APIs (high cost) or code-structure comparison (fails to handle code asymmetry) | Objectively and efficiently evaluates layout and style consistency based on rendered results | |
| | **Training Effectiveness** | Difficult to optimize on crawled data with asymmetric code structures | Proposed metrics can be directly used as RL reward signals to enhance model optimization | |
|
|
| --- |
|
|
| ### **Dataset Characteristics** |
|
|
| <p align="center"> |
| <img src="./docs/assets/framework.svg" alt="WebRenderBench and ALISA Framework" width="80%" /> |
| </p> |
| <p align="center"><i>Figure 1: Dataset construction pipeline and the ALISA framework</i></p> |
|
|
| Our dataset is constructed through a systematic process to ensure both **high quality** and **diversity**: |
|
|
| 1. **Data Collection**: URLs are obtained from open enterprise portal datasets. A high-concurrency crawler captures 210K webpages along with static resources. |
| 2. **Data Processing**: MHTML pages are converted into HTML files, and cross-domain resources are processed to ensure local renderability and full-page screenshots. |
| 3. **Data Cleaning**: Pages with abnormal sizes, rendering errors, or missing styles are filtered out. Multimodal QA models further remove low-quality samples with large blank areas or overlapping elements, yielding 110K valid pages. |
| 4. **Data Categorization**: Pages are categorized by industry and complexity (measured via *Group Count*) to ensure balanced distribution across difficulty levels and domains. |
|
|
| Finally, we construct a dataset of **45.1K** samples, evenly split into training and test sets. |
|
|
| --- |
|
|
| ## **🌟 Evaluation Framework** |
|
|
| We propose a novel evaluation protocol based on **rendered webpages**, quantifying model performance along two key dimensions: **layout** and **style consistency**. |
|
|
| --- |
|
|
| ### **RDA (Relative Layout Difference of Associated Elements)** |
|
|
| **Purpose:** Measures relative layout differences between matched elements. |
|
|
| * **Element Association:** Matches corresponding elements between generated and target pages using text similarity (LCS) and geometric distance. |
| * **Positional Deviation:** The page is divided into a 3×3 grid. Associated elements are compared quadrant-wise—if located in different quadrants, the score is 0; otherwise, a deviation-based score is computed. |
| * **Uniqueness Weighting:** Each element is weighted by its uniqueness (inverse group size), giving higher importance to distinctive components. |
|
|
| --- |
|
|
| ### **GDA (Group-wise Difference in Element Counts)** |
|
|
| **Purpose:** Measures group-level alignment of axis-aligned elements. |
|
|
| * **Grouping:** Elements aligned on the same horizontal or vertical axis are treated as one group. |
| * **Count Comparison:** Compares whether corresponding groups in the generated and target pages contain the same number of elements. |
| * **Uniqueness Weighting:** Weighted by element uniqueness to emphasize key structural alignment. |
|
|
| --- |
|
|
| ### **SDA (Style Difference of Associated Elements)** |
|
|
| **Purpose:** Evaluates fine-grained style differences between associated elements. |
|
|
| * **Multi-Dimensional Style Extraction:** Measures differences in foreground color, background color, font size, and border radius. |
| * **Weighted Averaging:** Computes a weighted mean of style similarity scores across all associated elements to obtain an overall style score. |
|
|
| --- |
|
|
| ## **⚙️ Installation Guide** |
|
|
| ### **Core Dependencies** |
|
|
| <!-- |
| # Recommended: Use vLLM for faster inference |
| pip install vllm transformers>=4.40.0 torch>=2.0 |
|
|
| # Other dependencies |
| pip install selenium pandas scikit-learn pillow |
|
|
| Alternatively: |
| pip install -r requirements.txt |
| --> |
|
|
| Coming Soon |
|
|
| --- |
|
|
| ## **📊 Benchmark Workflow** |
|
|
| ### **Directory Structure** |
|
|
| ``` |
| |- docs/ # Documentation |
| |- scripts # Evaluation scripts |
| |- web_render_test.jsonl # Test set metadata |
| |- web_render_train.jsonl # Training set metadata |
| |- test_webpages.zip # Test set webpages |
| |- train_webpages.zip # Training set webpages |
| |- test_screenshots.zip # Test set screenshots |
| |- train_screenshots.zip # Training set screenshots |
| ``` |
|
|
| --- |
|
|
| ### **Obtain Datasets** |
|
|
| - Webpages |
|
|
| | File Name | Download Link (ModelScope) | |
| |--------|---------------------| |
| | train_webpages.7z.001 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/train_webpages.7z.001) | |
| | train_webpages.7z.002 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/train_webpages.7z.002) | |
| | train_webpages.7z.003 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/train_webpages.7z.003) | |
| | train_webpages.7z.004 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/train_webpages.7z.004) | |
| | train_webpages.7z.005 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/train_webpages.7z.005) | |
| | train_webpages.7z.006 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/train_webpages.7z.006) | |
| | train_webpages.7z.007 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/train_webpages.7z.007) | |
| | train_webpages.7z.008 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/train_webpages.7z.008) | |
| | train_webpages.7z.009 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/train_webpages.7z.009) | |
| | train_webpages.7z.010 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/train_webpages.7z.010) | |
| | train_webpages.7z.011 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/train_webpages.7z.011) | |
| | train_webpages.7z.012 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/train_webpages.7z.012) | |
| | train_webpages.7z.013 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/train_webpages.7z.013) | |
| | train_webpages.7z.014 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/train_webpages.7z.014) | |
| | train_webpages.7z.015 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/train_webpages.7z.015) | |
| | train_webpages.7z.016 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/train_webpages.7z.016) | |
| | train_webpages.7z.017 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/train_webpages.7z.017) | |
| | train_webpages.7z.018 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/train_webpages.7z.018) | |
| | train_webpages.7z.019 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/train_webpages.7z.019) | |
| | test_webpages.7z.001 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/test_webpages.7z.001) | |
| | test_webpages.7z.002 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/test_webpages.7z.002) | |
| | test_webpages.7z.003 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/test_webpages.7z.003) | |
| | test_webpages.7z.004 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/test_webpages.7z.004) | |
| | test_webpages.7z.005 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/test_webpages.7z.005) | |
| | test_webpages.7z.006 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/test_webpages.7z.006) | |
| | test_webpages.7z.007 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/test_webpages.7z.007) | |
| | test_webpages.7z.008 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/test_webpages.7z.008) | |
| | test_webpages.7z.009 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/test_webpages.7z.009) | |
| | test_webpages.7z.010 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/test_webpages.7z.010) | |
| | test_webpages.7z.011 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/test_webpages.7z.011) | |
| | test_webpages.7z.012 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/test_webpages.7z.012) | |
| | test_webpages.7z.013 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/test_webpages.7z.013) | |
| | test_webpages.7z.014 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/test_webpages.7z.014) | |
| | test_webpages.7z.015 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/test_webpages.7z.015) | |
| | test_webpages.7z.016 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/test_webpages.7z.016) | |
| | test_webpages.7z.017 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/test_webpages.7z.017) | |
| | test_webpages.7z.018 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/test_webpages.7z.018) | |
| |
| - Screenshots |
| |
| | File Name | Download Link (ModelScope) | |
| |--------|---------------------| |
| | train_screenshots.7z.001 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/train_screenshots.7z.001) | |
| | train_screenshots.7z.002 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/train_screenshots.7z.002) | |
| | test_screenshots.7z.001 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/test_screenshots.7z.001) | |
| | test_screenshots.7z.002 | [Download](https://www.modelscope.cn/datasets/lpc1290/WebRenderBench/resolve/master/test_screenshots.7z.002) | |
| |
| ### **Implementation Steps** |
| |
| 1. **Data Preparation** |
| |
| * Download the WebRenderBench dataset and extract webpage and screenshot archives. |
| * Each pair consists of a real webpage (HTML + resources) and its rendered screenshot. |
| |
| 2. **Model Inference** |
| |
| * Run inference using engines such as **vLLM** or **LLM Deploy**, and save results to the designated directory. |
| |
| 3. **Evaluation** |
| |
| * Run `scripts/1_get_evaluation.py`. |
| * The script launches a web server to render both generated and target HTML. |
| * WebDriver extracts DOM information and computes **RDA**, **GDA**, and **SDA** scores. |
| * Results are saved under `save_results/`. |
| * Final scores are aggregated via `scripts/2_compute_alisa_scores.py`. |
|
|
| 4. **ALISA Training (Optional)** |
|
|
| * Use `models/train_rl.py` for reinforcement learning fine-tuning. *(Coming Soon)* |
| * The computed evaluation scores serve as reward signals to optimize policy models via methods such as **GRPO**. |
|
|
| --- |
|
|
| ## **📈 Model Performance Insights** |
|
|
| We evaluate **17 multimodal large language models** of varying scales and architectures (both open- and closed-source). |
|
|
| * **Combined Scores of RDA, GDA, and SDA (%)** |
|
|
|  |
|
|
| **Key Findings:** |
|
|
| * Overall, larger models achieve higher consistency. **GPT-4.1-mini** and **Qwen-VL-Plus** perform best among closed-source models. |
| * While most models perform reasonably on simple pages (*Group Count* < 50), **RDA scores drop sharply** as page complexity increases—precise layout alignment remains a major challenge. |
| * After reinforcement learning via the **ALISA framework**, **Qwen2.5-VL-7B** shows substantial improvements across all complexity levels, even surpassing **GPT-4.1-mini** on simpler cases. |
|
|
| --- |
|
|
| ## **📅 Future Work** |
|
|
| * [ ] Release pretrained models fine-tuned with the ALISA framework |
| * [ ] Expand dataset coverage to more industries and dynamic interaction patterns |
| * [ ] Open-source the complete toolchain for data collection, cleaning, and evaluation |
|
|
| --- |
|
|
| ## **📜 License** |
|
|
| The **WebRenderBench dataset** is released for **research purposes only**. |
| All accompanying code will be published under the **Apache License 2.0**. |
|
|
| All webpages in the dataset are collected from publicly accessible enterprise portals. |
| To protect privacy, all personal and sensitive information has been removed or modified. |
|
|
| --- |
|
|
| ## **📚 Citation** |
|
|
| If you use our dataset or framework in your research, please cite the following paper: |
|
|
| ```bibtex |
| @article{webrenderbench2025, |
| title={WebRenderBench: Enhancing Web Interface Generation through Layout-Style Consistency and Reinforcement Learning}, |
| author={Anonymous Author(s)}, |
| year={2025}, |
| journal={arXiv preprint}, |
| } |
| ``` |
