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This paper introduces CTRL-S, a novel framework for SVG generation that leverages chain-of-thought reasoning and reinforcement learning to improve the structural coherence and visual fidelity of generated SVGs. They construct a new dataset, SVG-Sophia, containing 145K samples across SVG code refinement, Text-to-SVG, and Image-to-SVG tasks to support structured reasoning. CTRL-S employs the GRPO algorithm with a multi-reward optimization framework, incorporating DINO, image-text similarity, format, and code efficiency rewards, leading to state-of-the-art performance in SVG generation tasks.
By explicitly exposing the model's reasoning process during SVG generation, CTRL-S achieves higher task success rates, superior SVG code quality, and exceptional visual fidelity compared to existing methods.
With the rapid advancement of vision-language models, an increasing number of studies have explored their potential for SVG generation tasks. Although existing approaches improve performance by constructing large-scale SVG datasets and introducing SVG-specific tokens, they still suffer from limited generalization, redundant paths in code outputs, and a lack of explicit reasoning. In this work, we present CTRL-S (Chain-of-Thought Reinforcement Learning for SVG), a unified framework that introduces a chain-of-thought mechanism to explicitly expose the model's reasoning process during SVG generation. To support this structured reasoning, we construct SVG-Sophia, a high-quality dataset containing 145K samples across SVG code refinement, Text-to-SVG, and Image-to-SVG tasks. By training the model to generate group-level structured SVG code, CTRL-S significantly improves structural coherence and visual fidelity. Furthermore, we adopt the GRPO algorithm and design a multi-reward optimization framework, incorporating DINO, image-text similarity, format, and code efficiency rewards. Through joint multi-reward optimization and multi-task training, our approach systematically enhances overall generation capabilities. Extensive experiments show that CTRL-S outperforms existing methods, achieving higher task success rates, superior SVG code quality, and exceptional visual fidelity.
