Reliable Reasoning in SVG-LLMs via Multi-Task Multi-Reward Reinforcement Learning
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.
69ba11585d2b9c28ea09f0c6
Haomin Wang
claimed_verified
2026-03-23T10:58:15.909Z
64d83ee0763279bb4ddd5ba8
https://cdn-avatars.huggingface.co/v1/production/uploads/64d83ee0763279bb4ddd5ba8/exbvc1hhIXXKPVmJpxRtP.jpeg
Haomin Wang
KiyotakaWang
user
KiyotakaWang
69ba11585d2b9c28ea09f0c7
Qi Wei
69ba11585d2b9c28ea09f0c8
Qianli Ma
claimed_verified
2026-03-18T08:36:14.824Z
6448b2f53e7b3c11be684348
https://cdn-avatars.huggingface.co/v1/production/uploads/6448b2f53e7b3c11be684348/QvlUQG3pWf8ZyEVBV6F7w.jpeg
Qianli Ma
Mqleet
user
Mqleet
69ba11585d2b9c28ea09f0c9
Shengyuan Ding
69ba11585d2b9c28ea09f0ca
Jinhui Yin
69ba11585d2b9c28ea09f0cb
Kai Chen
69ba11585d2b9c28ea09f0cc
Hongjie Zhang
Reliable Reasoning in SVG-LLMs via Multi-Task Multi-Reward Reinforcement Learning
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.
CTRL-S framework enhances SVG generation through chain-of-thought reasoning and multi-reward optimization, achieving better structural coherence and visual fidelity.
vision-language models SVG generation chain-of-thought mechanism SVG-Sophia dataset GRPO algorithm multi-reward optimization DINO image-text similarity code efficiency multi-task training