Reliable Reasoning in SVG-LLMs via Multi-Task Multi-Reward Reinforcement Learning

With the rapid advancement of vision–language models, anincreasing number of studies have explored their potential for SVG gener-ation tasks. Although existing approaches improve performance by con-structing large-scale SVG datasets and introducing SVG-specific tokens,they still suffer from limited generalization, redundant paths in code out-puts, and a lack of explicit reasoning. In this work, we present CTRL-S(Chain-of-Thought Reinforcement Learning for SVG), a unified frame-work that introduces a chain-of-thought mechanism to explicitly exposethe model’s reasoning process during SVG generation. To support thisstructured reasoning, we construct SVG-Sophia, a high-quality datasetcontaining 145K samples across SVG code refinement, Text-to-SVG, andImage-to-SVG tasks. By training the model to generate group-level struc-tured SVG code, CTRL-S significantly improves structural coherenceand visual fidelity. Furthermore, we adopt the GRPO algorithm and de-sign a multi-reward optimization framework, incorporating DINO, im-age–text similarity, format, and code efficiency rewards. Through jointmulti-reward optimization and multi-task training, our approach system-atically enhances overall generation capabilities. Extensive experimentsshow that CTRL-S outperforms existing methods, achieving higher tasksuccess rates, superior SVG code quality, and exceptional visual fidelity.Our code is available at https://github.com/hmwang2002/CTRL-S.