Planning Before Drawing: Dual-Expert On-Policy Distillation for Text-to-SVG

Text-to-SVG generation aims to translate textual descriptions into executable SVG code that produces visually faithful and editable vector graphics. A key challenge is to preserve visual fidelity while organizing low-level drawing commands into meaningful semantic components that better reflect the compositional structure of the depicted content. Existing methods improve SVG generation through large scale data, SVG specific representations, and reinforcement learning, yet typically learn semantic organization and geometric realization within a single model. Since planning what to draw and realizing how to draw involve distinct objectives, joint optimization can cause competing supervision and limit both capabilities. To address these, we propose DEFT, a dual expert framework that separates planning from drawing and then integrates their complementary knowledge. We first construct SVGVerse, a semantically structured SVG dataset, and perform two stage supervised fine tuning to establish correspondences among textual descriptions, semantic components, and drawing commands. We then optimize a Planning Expert for semantic organization and a Drawing Expert for geometric realization, followed by Command Routed On-Policy Distillation that selectively transfers their expertise according to the semantic roles of SVG commands. Extensive experiments demonstrate that our method achieves superior performance over traditional SVG generation methods, zero-shot models, and specialized SVG-LLMs.