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🧩 SAgoge Dataset: https://huggingface.co/datasets/InternSVG/SAgoge
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🤖 InternSVG-8B Model: https://huggingface.co/InternSVG/InternSVG-8B
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🧩 SAgoge Dataset: https://huggingface.co/datasets/InternSVG/SAgoge
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🤖 InternSVG-8B Model: https://huggingface.co/InternSVG/InternSVG-8B
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## Reliable Reasoning in SVG-LLMs via Multi-Task Multi-Reward Reinforcement Learning
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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 of 145K samples across SVG code refinement, Text-to-SVG, and Image-to-SVG tasks. Furthermore, we design a robust multi-reward reinforcement learning scheme powered by the GRPO algorithm. By jointly optimizing across DINO, image-text similarity, format, and code efficiency rewards in a multi-task setting, our approach systematically boosts structural coherence and generation capabilities. Extensive experiments show that CTRL-S outperforms existing methods, achieving higher task success rates, superior code quality, and exceptional visual fidelity.
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📄 ArXiv Paper: https://arxiv.org/abs/2603.16189
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💻 GitHub Repository: https://github.com/hmwang2002/CTRL-S
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🧩 SVG-Sophia Dataset: https://huggingface.co/datasets/InternSVG/SVG-Sophia
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