@inproceedings{rodriguez-etal-2026-vectorgym,
title = "{V}ector{G}ym: A Multi-Task Benchmark for {SVG} Code Generation, Sketching and Editing",
author = "Rodriguez, Joan and
Zhang, Haotian and
Puri, Abhay and
Dai, Haoran and
Zhang, Tianyang and
Lin, Meng and
Pramanik, Rishav and
Xie, Xiaoqing and
Rodriguez, Marco Terral and
Kaushik, Darsh and
Shariff, Aly and
Rajeswar, Sai and
Taslakian, Perouz and
Gella, Spandana and
Vazquez, David and
Pal, Christopher and
Pedersoli, Marco",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.820/",
pages = "19064--19091",
ISBN = "979-8-89176-459-0",
abstract = "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 provide a multi-task reinforcement learning baseline that jointly optimizes across all four tasks using rendering-based rewards. This baseline, 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."
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<abstract>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 provide a multi-task reinforcement learning baseline that jointly optimizes across all four tasks using rendering-based rewards. This baseline, 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.</abstract>
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%0 Conference Proceedings
%T VectorGym: A Multi-Task Benchmark for SVG Code Generation, Sketching and Editing
%A Rodriguez, Joan
%A Zhang, Haotian
%A Puri, Abhay
%A Dai, Haoran
%A Zhang, Tianyang
%A Lin, Meng
%A Pramanik, Rishav
%A Xie, Xiaoqing
%A Rodriguez, Marco Terral
%A Kaushik, Darsh
%A Shariff, Aly
%A Rajeswar, Sai
%A Taslakian, Perouz
%A Gella, Spandana
%A Vazquez, David
%A Pal, Christopher
%A Pedersoli, Marco
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F rodriguez-etal-2026-vectorgym
%X 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 provide a multi-task reinforcement learning baseline that jointly optimizes across all four tasks using rendering-based rewards. This baseline, 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.
%U https://aclanthology.org/2026.emnlp-main.820/
%P 19064-19091
Markdown (Informal)
[VectorGym: A Multi-Task Benchmark for SVG Code Generation, Sketching and Editing](https://aclanthology.org/2026.emnlp-main.820/) (Rodriguez et al., EMNLP 2026)
ACL
- Joan Rodriguez, Haotian Zhang, Abhay Puri, Haoran Dai, Tianyang Zhang, Meng Lin, Rishav Pramanik, Xiaoqing Xie, Marco Terral Rodriguez, Darsh Kaushik, Aly Shariff, Sai Rajeswar, Perouz Taslakian, Spandana Gella, David Vazquez, Christopher Pal, and Marco Pedersoli. 2026. VectorGym: A Multi-Task Benchmark for SVG Code Generation, Sketching and Editing. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 19064–19091, Budapest, Hungary. Association for Computational Linguistics.