title: Rendering-Aware Reinforcement Learning for Vector Graphics Generation
summary: 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.
paper_authors._id: 68372ea2ad0fc935fff6bd1e
paper_authors.name: Juan A. Rodriguez
paper_authors.status: claimed_verified
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paper_authors.fullname: Juan A. Rodriguez
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paper_authors.user: joanrodai
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paper_authors.name: Haotian Zhang
paper_authors.status: claimed_verified
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paper_authors._id: 63163a2d95b55e2621d23e4d
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paper_authors.fullname: Haotian Zhang
paper_authors.type: user
paper_authors.user: hz2475
paper_authors._id: 68372ea2ad0fc935fff6bd20
paper_authors.name: Abhay Puri
paper_authors._id: 68372ea2ad0fc935fff6bd21
paper_authors.name: Aarash Feizi
paper_authors._id: 68372ea2ad0fc935fff6bd22
paper_authors.name: Rishav Pramanik
paper_authors.status: claimed_verified
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paper_authors.fullname: Rishav Pramanik
paper_authors.type: user
paper_authors.user: rishavpramanik
paper_authors._id: 68372ea2ad0fc935fff6bd23
paper_authors.name: Pascal Wichmann
paper_authors._id: 68372ea2ad0fc935fff6bd24
paper_authors.name: Arnab Mondal
paper_authors._id: 68372ea2ad0fc935fff6bd25
paper_authors.name: Mohammad Reza Samsami
paper_authors._id: 68372ea2ad0fc935fff6bd26
paper_authors.name: Rabiul Awal
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-28T20:10:16.612Z
paper_authors._id: 60edf5e03203a5daf7d3912e
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paper_authors.fullname: Rabiul Awal
paper_authors.type: user
paper_authors.user: rabiulawal
paper_authors._id: 68372ea2ad0fc935fff6bd27
paper_authors.name: Perouz Taslakian
paper_authors._id: 68372ea2ad0fc935fff6bd28
paper_authors.name: Spandana Gella
paper_authors._id: 68372ea2ad0fc935fff6bd29
paper_authors.name: Sai Rajeswar
paper_authors._id: 68372ea2ad0fc935fff6bd2a
paper_authors.name: David Vazquez
paper_authors._id: 68372ea2ad0fc935fff6bd2b
paper_authors.name: Christopher Pal
paper_authors._id: 68372ea2ad0fc935fff6bd2c
paper_authors.name: Marco Pedersoli
paper_title: Rendering-Aware Reinforcement Learning for Vector Graphics Generation
paper_summary: 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.
paper_ai_summary: RLRF, a reinforcement learning method utilizing rendering feedback, enhances SVG generation in VLMs, improving accuracy and efficiency by comparing rendered SVGs to original images.
paper_ai_keywords: vision-language models code generation large-scale pretraining global semantics fine-grained visual patterns differentiable rendering reinforcement learning autoregressive VLMs SVG roll-outs visual fidelity feedback supervised fine-tuning structural understanding generalization