Code Aesthetics with Agentic Reward Feedback
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.
690061a922d452aac6dd444a
Bang Xiao
claimed_verified
2026-01-15T15:07:53.993Z
665e5c081f390ed0370679ab
/avatars/d53d44984f1ca56776d6b145db3230f3.svg
Bang Xiao
SamuelBang
user
SamuelBang
690061a922d452aac6dd444b
Lingjie Jiang
690061a922d452aac6dd444c
Shaohan Huang
690061a922d452aac6dd444d
Tengchao Lv
690061a922d452aac6dd444e
Yupan Huang
690061a922d452aac6dd444f
Xun Wu
690061a922d452aac6dd4450
Lei Cui
690061a922d452aac6dd4451
Furu Wei
Code Aesthetics with Agentic Reward Feedback
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.
A new pipeline enhances LLM-generated code aesthetics using a large-scale dataset, multi-agent feedback, and integrated optimization, outperforming existing models.
Large Language Models LLMs code aesthetics AesCode-358K agentic reward feedback multi-agent system GRPO-AR GRPO algorithm OpenDesign supervised fine-tuning reinforcement learning PandasPlotBench AesCoder-4B GPT-4o GPT-4.1