Improving LLM SVG Generation with Reinforcement Learning
Published:
Overview
Open-source LLMs struggle to generate semantically meaningful and aesthetic SVG graphics from text descriptions. This project applies state-of-the-art reinforcement learning techniques to dramatically improve their capabilities.
Method
I used Group Relative Policy Optimization (GRPO) with a custom 3-part reward function that encourages:
- Structured outputs: Valid SVG code with proper syntax
- Aesthetic quality: Visually appealing graphics
- Semantic alignment: Match to input text descriptions
The model (Qwen-2.5 7B) learns through trial and error, similar to recent approaches like DeepSeek-R1.
Results
- 18% improvement on a comprehensive benchmark evaluating SVG aesthetics, alignment, and code validity
- Significantly better structured outputs compared to base model
- Demonstrates how RL can improve visual reasoning in LLMs
Technical Details
- Model: Qwen-2.5 7B
- RL Algorithm: Group Relative Policy Optimization
- Framework: PyTorch with custom reward functions