# Code Aesthetics with Agentic Reward Feedback

^1,2Bang Xiao^#, ^1,3Lingjie Jiang^#, ¹Shaohan Huang^✉, ¹Tengchao Lv, ¹Yupan Huang, ¹Xun Wu, ¹Lei Cui, ¹Furu Wei

¹Microsoft Research Asia ²Zhiyuan College, Shanghai Jiao Tong University ³Peking University
^#Equal Contribution ^✉Corresponding author

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 our AesCoder model - [ ] Release code **Note: This is the version of AesCoder-4B model for only webpage design.** ## Quickstart ### VLLM deployment (Recommended) We recommend using `vllm>=0.8.5` for efficient inference and deployment. Here's how to get started: **Installation:** ```bash pip install vllm>=0.8.5 ``` **API Server Deployment:** To create an OpenAI-compatible API endpoint: ```bash vllm serve SamuelBang/AesCoder-4B --max-model-len 262144 ``` **Using with OpenAI Client:** ```python from openai import OpenAI # Initialize the client client = OpenAI( base_url="http://localhost:8000/v1", api_key="None" ) # Generate completion response = client.chat.completions.create( model="SamuelBang/AesCoder-4B", messages=[ {"role": "user", "content": "Create a user-friendly website for a landing page dedicated to selling dog-related products."} ], temperature=0.8, max_tokens=16384 ) # Get the generated content content = response.choices[0].message.content print("Generated content:", content) ``` **Basic vLLM Usage:** ```python from vllm import LLM, SamplingParams model_name = "SamuelBang/AesCoder-4B" # Initialize the model llm = LLM( model=model_name, max_model_len=262144, # Maximum context length tensor_parallel_size=1, # Adjust based on your GPU setup ) # Define sampling parameters sampling_params = SamplingParams( temperature=0.8, top_p=0.8, top_k=20, min_p=0, max_tokens=16384 ) # Prepare the prompt prompt = "Create a user-friendly website for a landing page dedicated to selling dog-related products. " messages = [ {"role": "user", "content": prompt} ] # Apply chat template (you'll need to get the tokenizer for this) from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained(model_name) text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, ) # Generate text outputs = llm.generate([text], sampling_params) # Get the result content = outputs[0].outputs[0].text print("Generated content:", content) ``` **Note:** If you encounter out-of-memory (OOM) issues, consider reducing the context length to a shorter value, such as `32,768` or adjusting the `tensor_parallel_size` based on your available GPU memory. ### SGLang Deployment You can use `sglang>=0.4.6.post1` to create an OpenAI-compatible API endpoint: ```shell python -m sglang.launch_server --model-path SamuelBang/AesCoder-4B --context-length 262144 ``` **Note: If you encounter out-of-memory (OOM) issues, consider reducing the context length to a shorter value, such as `32,768`.** ### Use with origin `transformer` package (NOT recommended, very slow) The code of Qwen3 has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`. With `transformers<4.51.0`, you will encounter the following error: ``` KeyError: 'qwen3' ``` The following contains a code snippet illustrating how to use the model generate content based on given inputs. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "SamuelBang/AesCoder-4B" # load the tokenizer and the model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) # prepare the model input prompt = "Create a user-friendly website for a landing page dedicated to selling dog-related products. " messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # conduct text completion generated_ids = model.generate( **model_inputs, max_new_tokens=16384, temperature=0.8 ) output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() content = tokenizer.decode(output_ids, skip_special_tokens=True) print("content:", content) ``` ## Best Practices ### Sampling Parameters To achieve optimal performance, we suggest using `Temperature=0.8`, `TopP=0.8`, `TopK=20`, and `MinP=0` for sampling. ### System Prompt To achieve better webpage generation results with our model, please use appropriate system prompts. We have categorized webpage generation into 5 main categories, and the recommended system prompts for each category are as follows (reference to: https://designarena.ai): Website: ```txt You are an expert web developer and designer specializing in modern websites. Create a complete, working HTML page with embedded CSS and JavaScript if needed. Feel free to use lightweight libraries like Tailwind CSS to enhance the design as long as they can be rendered in an iframe. Requirements: 1. Create a fully functional, modern, and responsive website design 2. Use only HTML, CSS, and JavaScript, but feel free to use libraries like Tailwind CSS to make the design better. Libraries such as ThreeJS, react three fiber, drei, @react-three/postprocessing, @react-three/cannon, d3, and recharts as additional libraries can be imported. 3. Include all styles inline within