RULER: Instance-aware Rubric Rewards for SVG Generation
RULER uses per-instruction rubric rewards so reinforcement learning improves text-to-SVG generation without ground truth.
RULER is a reinforcement-learning method that scores SVG generation with instance-aware rubrics instead of scalar image metrics that transfer poorly to vector art. Each instruction becomes a six-item rubric covering semantic, visual, and stylistic axes; a vision-language judge scores rendered rollouts, and weighted scores train the policy with Group Relative Policy Optimization. No paired SVG ground truth or human preference labels are required. On MMSVG-Illustration and MMSVG-Icon, rubric scores rise from 0.432 and 0.395 to 0.693 and 0.683, matching much larger DeepSeek-V3.
- RULER builds a six-item instance-aware rubric from each text instruction alone.
- A vision-language judge scores rendered SVG rollouts item by item for Group Relative Policy Optimization.
- Rubric scores rise from 0.432/0.395 to 0.693/0.683 on MMSVG-Illustration and MMSVG-Icon.
- Results beat dedicated SVG specialists and match the much larger DeepSeek-V3.
- Vendors
- DeepSeek
- AI models
- DeepSeek-V3RULER
From the original article
1 min read at huggingface.co
Generating Scalable Vector Graphics (SVG) code from natural-language instructions is an open-ended task without absolute visual ground truth, leaving both evaluation and policy optimization without a faithful signal. Scalar metrics (CLIP, Aesthetic) calibrated on natural images transfer poorly to stylized vector content, and reusing them as RL rewards triggers reward hacking.
By Hangyu Ran, Yuhao Zheng, Yingying Zhang · huggingface.co
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