VectorGym: A Multitask Benchmark for SVG Code Generation, Sketching, and Editing
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
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2026
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| author | Rodriguez, Juan Zhang, Haotian Puri, Abhay Zhang, Tianyang Pramanik, Rishav Lin, Meng Xie, Xiaoqing Terral, Marco Kaushik, Darsh Shariff, Aly Taslakian, Perouz Gella, Spandana Rajeswar, Sai Vazquez, David Pal, Christopher Pedersoli, Marco |
| author_facet | Rodriguez, Juan Zhang, Haotian Puri, Abhay Zhang, Tianyang Pramanik, Rishav Lin, Meng Xie, Xiaoqing Terral, Marco Kaushik, Darsh Shariff, Aly Taslakian, Perouz Gella, Spandana Rajeswar, Sai Vazquez, David Pal, Christopher Pedersoli, Marco |
| contents | We introduce VectorGym, a comprehensive benchmark suite for Scalable Vector Graphics (SVG) that spans generation from text and sketches, complex editing, and visual understanding. VectorGym addresses the lack of realistic, challenging benchmarks aligned with professional design workflows. Our benchmark comprises four tasks with expert human-authored annotations: the novel Sketch2SVG task (VG-Sketch); a new SVG editing dataset (VG-Edit) featuring complex, multi-step edits with higher-order primitives; Text2SVG generation (VG-Text); and SVG captioning (VG-Cap). Unlike prior benchmarks that rely on synthetic edits, VectorGym provides gold-standard human annotations that require semantic understanding and design intent. We also propose a multi-task reinforcement learning approach that jointly optimizes across all four tasks using rendering-based rewards. Our method, built on GRPO with curriculum learning, trains a Qwen3-VL 8B model that achieves state-of-the-art performance among open-source models, surpassing much larger models including Qwen3-VL 235B and matching GPT-4o. We also introduce a VLM-as-a-Judge metric for SVG generation, validated through human correlation studies. Our evaluation of frontier VLMs reveals significant performance gaps, positioning VectorGym as a rigorous framework for advancing visual code generation. VectorGym is publicly available on huggingface.co/datasets/ServiceNow/VectorGym. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_29852 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | VectorGym: A Multitask Benchmark for SVG Code Generation, Sketching, and Editing Rodriguez, Juan Zhang, Haotian Puri, Abhay Zhang, Tianyang Pramanik, Rishav Lin, Meng Xie, Xiaoqing Terral, Marco Kaushik, Darsh Shariff, Aly Taslakian, Perouz Gella, Spandana Rajeswar, Sai Vazquez, David Pal, Christopher Pedersoli, Marco Graphics Artificial Intelligence Computer Vision and Pattern Recognition We introduce VectorGym, a comprehensive benchmark suite for Scalable Vector Graphics (SVG) that spans generation from text and sketches, complex editing, and visual understanding. VectorGym addresses the lack of realistic, challenging benchmarks aligned with professional design workflows. Our benchmark comprises four tasks with expert human-authored annotations: the novel Sketch2SVG task (VG-Sketch); a new SVG editing dataset (VG-Edit) featuring complex, multi-step edits with higher-order primitives; Text2SVG generation (VG-Text); and SVG captioning (VG-Cap). Unlike prior benchmarks that rely on synthetic edits, VectorGym provides gold-standard human annotations that require semantic understanding and design intent. We also propose a multi-task reinforcement learning approach that jointly optimizes across all four tasks using rendering-based rewards. Our method, built on GRPO with curriculum learning, trains a Qwen3-VL 8B model that achieves state-of-the-art performance among open-source models, surpassing much larger models including Qwen3-VL 235B and matching GPT-4o. We also introduce a VLM-as-a-Judge metric for SVG generation, validated through human correlation studies. Our evaluation of frontier VLMs reveals significant performance gaps, positioning VectorGym as a rigorous framework for advancing visual code generation. VectorGym is publicly available on huggingface.co/datasets/ServiceNow/VectorGym. |
| title | VectorGym: A Multitask Benchmark for SVG Code Generation, Sketching, and Editing |
| topic | Graphics Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.29852 |