Rendering-Aware Reinforcement Learning for Vector Graphics Generation
Authors: name: Juan A. Rodriguez, name: Haotian Zhang, name: Abhay Puri, name: Aarash Feizi, name: Rishav Pramanik, name: Pascal Wichmann, name: Arnab Mondal, name: Mohammad Reza Samsami, name: Rabiul Awal, name: Perouz Taslakian, name: Spandana Gella, name: Sai Rajeswar, name: David Vazquez, name: Christopher Pal, name: Marco Pedersoli

Scalable Vector Graphics (SVG) offer a powerful format for representing
visual designs as interpretable code. Recent advances in vision-language models
(VLMs) have enabled high-quality SVG generation by framing the problem as a
code generation task and leveraging large-scale pretraining. VLMs are
particularly suitable for this task as they capture both global semantics and
fine-grained visual patterns, while transferring knowledge across vision,
natural language, and code domains. However, existing VLM approaches often
struggle to produce faithful and efficient SVGs because they never observe the
rendered images during training. Although differentiable rendering for
autoregressive SVG code generation remains unavailable, rendered outputs can
still be compared to original inputs, enabling evaluative feedback suitable for
reinforcement learning (RL). We introduce RLRF(Reinforcement Learning from
Rendering Feedback), an RL method that enhances SVG generation in
autoregressive VLMs by leveraging feedback from rendered SVG outputs. Given an
input image, the model generates SVG roll-outs that are rendered and compared
to the original image to compute a reward. This visual fidelity feedback guides
the model toward producing more accurate, efficient, and semantically coherent
SVGs. RLRF significantly outperforms supervised fine-tuning, addressing common
failure modes and enabling precise, high-quality SVG generation with strong
structural understanding and generalization.