Duplicate from SamuelBang/AesCode-358K

Co-authored-by: Bang Xiao <SamuelBang@users.noreply.huggingface.co>

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README.md

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+---
+task_categories:
+- text-generation
+tags:
+- code
+size_categories:
+- 100K<n<1M
+---
+# [ICLR 2026] Code Aesthetics with Agentic Reward Feedback
+<div align="center">
+<a href='https://bangx7.github.io/code-aesthetics/'><img src='https://img.shields.io/badge/Project-Page-Green'></a>
+  <a href="https://huggingface.co/SamuelBang/AesCoder-4B"><img alt="Hugging Face"
+    src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-ffc107?color=ffc107&logoColor=white"/></a>
+  <br>
+  <a href="https://arxiv.org/abs/2510.23272"><b>Paper Link</b>👁️</a>
+</div>
+<div align="center">
+<p>
+<sup>1,2</sup><a href="https://bangx7.github.io" target="_blank">Bang Xiao</a><sup>#</sup>,</span>
+                <span class="author-block">
+                  <sup>1,3</sup><a href="https://github.com/JackLingjie" target="_blank">Lingjie Jiang</a><sup>#</sup>,</span>
+                  <span class="author-block">
+                    <sup>1</sup><a href="https://www.microsoft.com/en-us/research/people/shaohanh/" target="_blank">Shaohan Huang</a><sup>✉</sup>,</span>
+                    <span class="author-block">
+                      <sup>1</sup><a href="https://www.microsoft.com/en-us/research/people/tengchaolv/" target="_blank">Tengchao Lv</a>,
+                    </span>
+                    <span class="author-block">
+                      <sup>1</sup><a href="https://www.microsoft.com/en-us/research/people/yupanhuang/" target="_blank">Yupan Huang</a>,
+                    </span>
+                    <span class="author-block">
+                      <sup>1</sup><a href="https://yushuiwx.github.io/" target="_blank">Xun Wu</a>,
+                    </span>
+                    <span class="author-block">
+                      <sup>1</sup><a href="https://www.microsoft.com/en-us/research/people/lecu/" target="_blank">Lei Cui</a>,
+                    </span>
+                    <span class="author-block">
+                      <sup>1</sup><a href="https://www.microsoft.com/en-us/research/people/fuwei/" target="_blank">Furu Wei</a>
+                    </span>
+</p>
+  <p>
+    <sup>1</sup>Microsoft Research Asia &nbsp;&nbsp;
+    <sup>2</sup>Zhiyuan College, Shanghai Jiao Tong University &nbsp;&nbsp;
+    <sup>3</sup>Peking University<br>
+    <sup>#</sup>Equal Contribution
+    <sup>✉</sup>Corresponding author
+  </p>
+</div>
+
+For the codebase, refer to: https://github.com/bangx7/code_aesthetics
+
+## 🎉 News
+- __[2026.01.12]__: Release the [AesCode](https://huggingface.co/datasets/SamuelBang/AesCode-358K) dataset.
+- __[2025.10.29]__: Release the [AesCoder-4B](https://huggingface.co/SamuelBang/AesCoder-4B/) model.
+- __[2025.10.27]__: Release the [Project Page](https://bangx7.github.io/code-aesthetics/) and the [Arxiv](https://arxiv.org/abs/2510.23272) version.
+
+
+## 📷 Abstract
+Large Language Models (LLMs) have become valuable assistants for developers in code-related tasks. While LLMs excel at traditional programming tasks such as code generation and bug fixing, they struggle with visually-oriented coding tasks, often producing suboptimal aesthetics. In this paper, we introduce a new pipeline to enhance the aesthetic quality of LLM-generated code. We first construct AesCode-358K, a large-scale instruction-tuning dataset focused on code aesthetics. Next, we propose agentic reward feedback, a multi-agent system that evaluates executability, static aesthetics, and interactive aesthetics. Building on this, we develop GRPO-AR, which integrates these signals into the GRPO algorithm for joint optimization of functionality and code aesthetics. Finally, we develop OpenDesign, a benchmark for assessing code aesthetics. Experimental results show that combining supervised fine-tuning on AesCode-358K with reinforcement learning using agentic reward feedback significantly improves performance on OpenDesign and also enhances results on existing benchmarks such as PandasPlotBench. Notably, our AesCoder-4B surpasses GPT-4o and GPT-4.1, and achieves performance comparable to large open-source models with 480B-685B parameters, underscoring the effectiveness of our approach.
+
+## To-do List
+- [x] Release paper and project page
+- [x] Release our AesCoder model
+- [x] Release AesCode dataset
+- [x] Release code
+
+**Note: This is the version of Qwen3-Coder-480B-A35B-Instruct-FP8 generated AesCode dataset for only webpage design.** For GPT-5 generated data, please stay tuned!
+
+## Dataset Overview
+This dataset contains **5 categories** of webpage design: 
+- `General Website`: ~100k
+- `Data Visualization`: ~30k
+- `3D Design`: ~30k
+- `Game Development`: ~30k
+- `UI Component`: ~30k
+
+
+## &#x1F4DA; Citation
+If you find this codebase useful for your research, please use the following entry.
+```BibTeX
+@misc{xiao2025codeaestheticsagenticreward,
+      title={Code Aesthetics with Agentic Reward Feedback}, 
+      author={Bang Xiao and Lingjie Jiang and Shaohan Huang and Tengchao Lv and Yupan Huang and Xun Wu and Lei Cui and Furu Wei},
+      year={2025},
+      eprint={2510.23272},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2510.23272}, 
+}
+```

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