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Post-training with a cross-modal similarity reward lifts a 7B open LLM's SVG generation to frontier quality: its compositional score rises from 8.8 to 60.8 and its VQA score, 0.596, is the best of any model tested.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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Qwen-2.5-7B trained with reinforcement learning against SigLIP visual similarity scores writes SVG drawings that match text captions about as well as frontier models on several automated metrics.

T0 review reviewed 2026-08-05 challenge →

load-bearing objection Useful benchmark and a plausible RL recipe for SVG generation, but the headline 'on par with frontier' is not yet supported because the reward and evaluation share the same encoder family and no human evaluation is provided. the 4 major comments →

arxiv 2509.05208 v1 pith:QO4FQPWQ submitted 2025-09-05 cs.CV cs.LG

Symbolic Graphics Programming with Large Language Models

Yamei Chen , Haoquan Zhang , Yangyi Huang , Zeju Qiu , Kaipeng Zhang , Yandong Wen , Weiyang Liu This is my paper
classification cs.CV cs.LG
keywords symbolic graphics programmingSVG generationreinforcement learningverifiable rewardcross-modal alignmentlarge language modelscompositional benchmarkSGP-GenBench
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper asks whether large language models can do symbolic graphics programming — writing executable SVG code that renders into the image a prompt describes — and whether that ability can be trained in. To measure the skill, it builds SGP-GenBench, a benchmark testing object fidelity, scene fidelity, and compositionality (attribute binding, spatial relations, numeracy); on it, frontier proprietary models clearly beat open-weight models, and scores track general coding ability. To close the gap, the paper post-trains Qwen-2.5-7B with reinforcement learning whose only supervision is a verifiable reward: render the generated SVG, then measure how well the image matches the caption in SigLIP/CLIP embedding space and, when a reference image exists, DINO image-image similarity, gated by a format check that rejects non-renderable or text-rendering code. The result is a jump in the compositional score from 8.8 to 60.8, the best VQA score of any tested model (0.596), and behavior changes — finer object decomposition and added contextual details — that sampling from the base model would take roughly a million or more candidates to reproduce. The claim matters because, if right, it means visual knowledge can be injected into LLMs from pre-trained vision encoders alone, without expensive image-program annotation.

Core claim

The paper's central discovery, stated on its own terms, is that symbolic graphics programming is an elicit-able skill: reinforcement learning with verifiable cross-modal rewards can turn a 7-billion-parameter open model that generates unrecognizable SVGs into one whose outputs are semantically on par with frontier proprietary systems. No ground-truth SVG programs are used; the policy learns purely from scalar similarity signals. Concretely, the authors report that RL lifts the SGP-CompBench average from 8.8 to 60.8 — going from the weakest open model to the strongest among all open models — and achieves the best VQA score across every tested model, frontier included, at 0.596. Training-dynam

What carries the argument

One reward stack carries the whole argument. The total reward factorizes as r = r_fmt × (λ_text·r_text + λ_image·r_image): r_fmt is a binary format gate requiring the think-answer response structure and a successful render through CairoSVG, with SVG text-rendering tags banned to close a caption-verbatim loophole; r_text is the linear rescaling (cos+1)/2 of the cosine similarity between SigLIP text embeddings and image embeddings of the rendered SVG; r_image applies the same rescaling to DINOv2 cosine similarity between the render and a reference image when one exists. Policies are updated with GRPO, a critic-free variant of PPO used in rule-based RL. The load-bearing design move is that the

Load-bearing premise

The method stands or falls on whether the cosine similarity between a rendered SVG and its caption in SigLIP/CLIP embedding space is a true, non-gameable measure of whether the drawing fulfills the prompt — the authors already had to ban text rendering to close one loophole in this channel.

What would settle it

Prompt the trained model with captions that differ only by swapped attribute bindings ("a red cup to the left of a blue saucer" versus "a blue cup to the left of a red saucer"), render the SVGs, and check whether the high-reward outputs actually realize the binding. If the policy achieves similar similarity scores for swapped renders, the reward channel rewards semantic adjacency rather than the requested relation and the training signal is compromised; if it reliably draws the correct binding, the claim survives. A complementary check is Best-of-N sampling from the base model at N between 10^

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • Open-weight models at 7B scale can be brought to frontier-level symbolic graphics with roughly a thousand RL steps and no ground-truth SVG programs, only captions plus pre-trained vision encoders.
  • The gap between open and closed models on this task is substantially closed by training, not scale: the tuned 7B model posts the best VQA score of any tested model (0.596) and outperforms all other open models on the compositional benchmark.
  • RL induces measurable drawing strategies — finer decomposition of objects into primitives, contextual optional details, and viewBox-cropping — so trained behavior goes beyond literal prompt fulfillment.
  • Best-of-N analysis implies that matching RL's gains by sampling more from the base model would require on the order of a million to a hundred million candidates, far beyond practical compute.
  • Benchmark ordering tracks general coding ability, so the authors argue SGP generation can serve as a diagnostic lens on cross-modal grounding and on how LLMs map language to structured visual programs.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: if the global similarity reward is the binding constraint, then replacing or augmenting it with per-object or relation-checking rewards (for example, a VQA-based adjudicator) should push compositional scores further; the paper's fixed reward stack leaves this untested.
  • Editorial inference: the same loop could distill other visual priors into symbolic programs by swapping the reward encoder — say, a diffusion model's scorer — turning the paper's 'implicit distillation' framing into a general method for transferring visual knowledge into LLMs.
  • Editorial inference: the paper's own CLIP-vs-SigLIP color comparison shows that reward-encoder preferences are silently baked into the policy's style, so deployment of RL-tuned drawing models should include an audit of stylistic bias inherited from the chosen encoder.
  • Editorial inference: the learned viewBox-cropping behavior reveals that token-efficiency pressures shape drawing strategy; metrics that count only rendered pixels, alongside the reported program-level statistics, would capture this more directly.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper studies symbolic graphics programming: generating SVG code from natural-language captions. It introduces SGP-GenBench, with three evaluation components (COCO-val scenes, SGP-Object-val objects, SGP-CompBench composition) totaling about 5,100 prompts, and reports that frontier closed-source models outperform open-source models. It then proposes an RLVR-style post-training recipe (GRPO) in which the reward is a format-validity gate plus a rescaled SigLIP text-image cosine (Eq. 4.7), optionally augmented with a DINO image-image cosine (Eq. 4.9). Applied to Qwen-2.5-7B, the method reportedly raises SGP-CompBench average from 8.8 to 60.8 and achieves the best VQA score in Table 1 (0.596). The paper also analyzes training dynamics, including object decomposition, optional contextual details, viewBox-clipping behavior, and Best-of-N scaling.

Significance. If the reported gains are genuine, the paper makes a useful contribution: it provides a sizable benchmark for SVG generation, demonstrates a simple reward pipeline that improves a 7B open model substantially, and offers an interesting Best-of-N analysis suggesting RL is not trivially replaceable by decoding-time scaling. The training-dynamics analysis (object decomposition, optional contextual elements, and color-choice differences between CLIP and SigLIP rewards) is also informative. The main caveat is that the headline evaluation relies heavily on automated metrics that overlap with the reward family, and the compositional scores come from an unvalidated commercial judge; without independent verification, the central 'on par with frontier systems' claim remains plausible but not established.

major comments (4)
  1. [§5.2.1 / Eq. (4.7)] The reward used for RL is a rescaled SigLIP text-image cosine (Eq. 4.7), and the headline fidelity metric CLIP-Score is the same family of contrastive text-image similarity. The final model is trained with SigLIP Base/16-384 (§5.3.1), while Table 1 reports CLIP-Score averaged over two CLIP models. Thus a substantial part of the reported CLIP increase is expected even if the model only learns to exploit encoder-specific statistics rather than to draw semantically better. VQA-Score and HPS are less directly aligned with the reward, but VQA-Score itself depends on an unspecified VLM question/answer pipeline (§B.2). I recommend reporting a truly external semantic fidelity metric (e.g., human ratings on a held-out sample, or a VLM judge not in the contrastive-reward family), and reporting results separately for reward-family and non-reward-family metrics.
  2. [§B.1.3, Tables 2 and 8] The entire SGP-CompBench evaluation is produced by a single model, Gemini-2.5-Flash-Preview, with no validation against human judgments, no confidence intervals, and no analysis of judge agreement. The headline claim that the RL model improves compositional scores from 8.8 to 60.8 rests on this unvalidated judge. Because the judge is a commercial model whose behavior can change, and because the scoring rubrics (100/50/30/0) are coarse, I cannot assess whether the compositional improvements are real. I recommend validating the judge on a human-annotated subset (or using a fixed, openly available judge with published agreement statistics) and reporting per-item errors.
  3. [§D.3 and §E.4] The paper itself provides evidence that the SigLIP/CLIP reward family is gameable: it had to ban <text>, <tspan>, and <textPath> tags because rendering the caption verbatim inflated rewards (§D.3). Appendix E.4 then shows the trained model exploiting viewBox clipping in a non-obvious way. The viewBox behavior is not necessarily a flaw, but together these examples show that the policy can find reward-maximizing shortcuts that are not the intended drawing semantics. This strengthens the need for independent evaluation and for an explicit analysis of remaining reward-hacking strategies, rather than relying on automated similarity metrics alone.
  4. [Reproducibility / artifacts] The paper does not provide code, data, or model checkpoints; the project name 'SphereLab.ai/SGP-Gen' appears without a usable URL. For a benchmark paper and an RL recipe, reproducibility is load-bearing: the claims cannot be checked, and the exact evaluation prompts, judge versions, and generated SVGs are not inspectable. I recommend releasing the evaluation harness, the training pipeline, the generated outputs at the reported checkpoints, and (where licensing permits) the benchmark data.
minor comments (5)
  1. [§B.2] The VQA-Score description is underspecified: which VLM is used, how many questions are generated per image, and who creates the question-answer pairs? This should be documented to make the metric reproducible.
  2. [§5.3.1 / Table 3] The final reward stack is described as 'SigLIP Base/16-384' but it is not fully clear whether lambda_image is zero in the final model or whether a DINO term is also used. Please state the exact final reward coefficients.
  3. [§6.1 / Figure 6] The claim that Best-of-N would need 10^6–10^8 samples is based on a linear extrapolation of curves that appear to be measured only for N up to about 10^2. The out-of-range extrapolation should be clearly labeled as an assumption, not a measured result.
  4. [Table 1] Some entries in Table 1 are run together without spacing (e.g., '0.2620.3050.284'), which makes the table hard to read. Please format the table for clarity.
  5. [§A.2] The sentence 'This methodical approach allowed us to comprehensively evaluate the model’s ability to handle different quantities.to' contains a stray 'to'. Please fix.

Circularity Check

0 steps flagged

No circularity: the RL reward (SigLIP cosine) and the headline evaluation metrics (CLIP-Score, DINO-Score, VQA-Score, HPS v2) are distinct; the central claim does not reduce to the training objective.

full rationale

The paper's derivation chain is: (i) define a rule-based RL objective with reward r(s,c,x) = r_fmt(s) * (λ_text r_Text(s,c) + λ_image r_Image(s,x)) (Eq. 4.4); (ii) r_Text is a linearly rescaled SigLIP cosine similarity between caption and rendered image (Eqs. 4.6–4.7); (iii) r_Image is a DINO cosine similarity when a reference image is available (Eqs. 4.8–4.9); (iv) the final model is trained with SigLIP Base/16-384 only, as stated in Sec. 5.3.1: 'we report all final results using a fixed reward stack of SigLIP Base/16-384'. The evaluation metrics are CLIP-Score (averaged over CLIP ViT-B/32 and ViT-L/14), DINO-Score (averaged over four DINOv2 variants), VQA-Score, and HPS v2 (Sec. 3.2, Sec. B.2). Although CLIP-Score and the SigLIP reward are both cross-modal cosine similarities in contrastive embedding spaces, they use different pre-trained encoders; optimizing SigLIP does not by construction maximize CLIP-Score. DINO-Score is computed with DINOv2 models that were not part of the final reward stack, and VQA-Score uses an LLM-generated QA set answered by a VLM, which is independent of the SigLIP reward. HPS v2 is a learned human-preference predictor, also outside the reward stack. Thus the central claims—'substantially improves SVG generation quality and semantics' and 'performance on par with frontier systems'—are supported by metrics that are not merely the training objective renamed. The only self-citation with overlapping authors is [QLF+25] in the Introduction ('[QLF+25, ZCZL24a] have shown that LLMs possess semantic understanding of SGPs'), used as background motivation, not as the load-bearing argument for the RL improvement. The documented text-rendering ban (App. D.3) and the observed viewBox clipping behavior (App. E.4) indicate the perceptual reward is gameable in principle, and the paper closes one exploit and reports it; this is a reward-validity concern, not a circular derivation. No equation in the paper is shown to be equivalent to another by construction, and no fitted parameter is renamed as a prediction. Therefore the circularity score is 0.

Axiom & Free-Parameter Ledger

3 free parameters · 5 axioms · 0 invented entities

The central claim leans on the validity of embedding-similarity rewards, the reliability of a VLM judge, and the sufficiency of the COCO/MMSVG caption mixture. No fitted free parameters are involved; the listed hyperparameters are hand-set. No invented entities are introduced; SGP-GenBench is a dataset, not an entity.

free parameters (3)
  • Numeracy score weights (Total 0.2, Item 0.2, CPI 0.6) = 0.2/0.2/0.6
    Hand-set in Section 3.2; changes aggregate numeracy ranking and is used to report the headline 60.8 average.
  • Reward coefficients lambda_text / lambda_image = 1.0 / 1.0 (or 0 when no reference image)
    Hand-set in Section 4.2; no tuning reported; affects reward balance and all reported results.
  • Asymmetric GRPO clip bounds = clip_low=0.20, clip_high=0.28
    Chosen in Appendix D.2 following DAPO to prevent entropy collapse; a training hyperparameter, not fitted to evaluation.
axioms (5)
  • domain assumption Rendering via CairoSVG is deterministic and captures visual content faithfully.
    Section 4.2.1 uses renderer success as the format-validity gate; if rendering were not faithful, the reward images would not reflect the SVG semantics.
  • ad hoc to paper SigLIP/CLIP cross-modal cosine is a valid continuous reward for semantic correctness of a drawing.
    Eq. 4.6-4.7 define the text-image reward; the entire RL pipeline depends on this similarity being a trustworthy proxy for drawing quality.
  • ad hoc to paper DINOv2 cosine similarity between rendered image and reference image is a valid fidelity reward.
    Eq. 4.8-4.9 define the image-image reward; used when reference images are available, and in ablations.
  • domain assumption Gemini-2.5-Flash-Preview as judge reliably scores compositionality in place of human ratings.
    Appendix B.1.3 uses this model for all SGP-CompBench scores; no human calibration or inter-annotator agreement is reported.
  • domain assumption Pretrained LLMs already have SVG priors that RL can elicit without ground-truth program annotations.
    Section 4.3 argues RL can distill visual knowledge into the LLM; the method provides no ground-truth SVG targets.

reviewed 2026-08-05 · how reviews work

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Cite this review

Pith. "Pith review of Symbolic Graphics Programming with Large Language Models." pith.science (2026). https://pith.science/paper/QO4FQPWQ

@misc{pith2026250905208,
  author       = {Pith},
  title        = {Pith review of: Symbolic Graphics Programming with Large Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QO4FQPWQ}},
  note         = {Machine review of arXiv:2509.05208}
}
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read the original abstract

Large language models (LLMs) excel at program synthesis, yet their ability to produce symbolic graphics programs (SGPs) that render into precise visual content remains underexplored. We study symbolic graphics programming, where the goal is to generate an SGP from a natural-language description. This task also serves as a lens into how LLMs understand the visual world by prompting them to generate images rendered from SGPs. Among various SGPs, our paper sticks to scalable vector graphics (SVGs). We begin by examining the extent to which LLMs can generate SGPs. To this end, we introduce SGP-GenBench, a comprehensive benchmark covering object fidelity, scene fidelity, and compositionality (attribute binding, spatial relations, numeracy). On SGP-GenBench, we discover that frontier proprietary models substantially outperform open-source models, and performance correlates well with general coding capabilities. Motivated by this gap, we aim to improve LLMs' ability to generate SGPs. We propose a reinforcement learning (RL) with verifiable rewards approach, where a format-validity gate ensures renderable SVG, and a cross-modal reward aligns text and the rendered image via strong vision encoders (e.g., SigLIP for text-image and DINO for image-image). Applied to Qwen-2.5-7B, our method substantially improves SVG generation quality and semantics, achieving performance on par with frontier systems. We further analyze training dynamics, showing that RL induces (i) finer decomposition of objects into controllable primitives and (ii) contextual details that improve scene coherence. Our results demonstrate that symbolic graphics programming offers a precise and interpretable lens on cross-modal grounding.

Figures

Figures reproduced from arXiv: 2509.05208 by Haoquan Zhang, Kaipeng Zhang, Weiyang Liu, Yamei Chen, Yandong Wen, Yangyi Huang, Zeju Qiu.

Figure 1
Figure 1. Figure 1: Qualitative results of symbolic graphics programming. We use reinforcement learning with customized [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: (a) Symbolic controllability of SGPs: to generate a “regular octagon”, SGPs can deliver precise representation [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overview of the proposed SGP-GenBench and some examples. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: An illustration of the RL pipeline. Given a text description, we sample a group of SVG codes from the [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative comparison of SVGs generated by frontier LLMs and our RL-trained model. Our model [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Analysis of the Best-of-N performance of RL checkpoints. (a): Best-of-N curves for the SigLIP-Base score. (b): Gain over the first checkpoint; the horizontal intercept indicates the value of N required to match RL-trained later checkpoints. Each curve corresponds to a checkpoint at 30, 120, 210, 300, 630, and 900 RL steps. The y-axis shows the text-image similarity computed with SigLIP Base/16-384. improve… view at source ↗
Figure 7
Figure 7. Figure 7: Training dynamics of code complexity. Error bars reflect variability across prompts and sampling replicates. [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Although both express the concept of “motorcycle,” the early-stage model at training step 30 only divides [PITH_FULL_IMAGE:figures/full_fig_p014_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Examples of optional details generated without explicit prompting. (a) Given only “A group of people sit [PITH_FULL_IMAGE:figures/full_fig_p015_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Quantitative evolution of generated code structure over training. [PITH_FULL_IMAGE:figures/full_fig_p015_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Evolution of SVG element type distribution throughout training. The numbers behind each legend [PITH_FULL_IMAGE:figures/full_fig_p030_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Qualitative examples from the model that draws a full elephant whose body extends outside the [PITH_FULL_IMAGE:figures/full_fig_p031_12.png] view at source ↗

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Reference graph

Works this paper leans on

45 extracted references · 27 canonical work pages · cited by 2 Pith papers · 2 internal anchors

  1. [1]

    Program synthesis with large language models

    Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, et al. Program synthesis with large language models. arXiv preprint arXiv:2108.07732 , 2021

    Pith/arXiv arXiv 2021

  2. [2]

    Constitutional ai: Harmlessness from ai feedback

    Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al. Constitutional ai: Harmlessness from ai feedback. arXiv preprint arXiv:2212.08073 , 2022

    Pith/arXiv arXiv 2022

  3. [3]

    Deepsvg: A hierarchical generative network for vector graphics animation

    Alexandre Carlier, Martin Danelljan, Alexandre Alahi, and Radu Timofte. Deepsvg: A hierarchical generative network for vector graphics animation. volume 33, 2020

    work page 2020

  4. [4]

    Conditional Vector Graphics Generation for Music Cover Images

    Valeria Efimova, Ivan Jarsky, Ilya Bizyaev, and Andrey Filchenkov. Conditional vector graphics generation for music cover images. arXiv preprint arXiv:2205.07301 , 2022

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  5. [5]

    Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning

    Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948 , 2025

    Pith/arXiv arXiv 2025

  6. [6]

    Rlef: Grounding code llms in execution feedback with reinforcement learning

    Jonas Gehring, Kunhao Zheng, Jade Copet, Vegard Mella, Taco Cohen, and Gabriel Synnaeve. Rlef: Grounding code llms in execution feedback with reinforcement learning. In ICML , 2025

    work page 2025

  7. [7]

    Automatic unit test data generation and actor-critic reinforcement learning for code synthesis

    Philip Gorinski, Matthieu Zimmer, Gerasimos Lampouras, Derrick Goh Xin Deik, and Ignacio Iacobacci. Automatic unit test data generation and actor-critic reinforcement learning for code synthesis. In EMNLP , 2023

    work page 2023

  8. [8]

    T2i-compbench++: An enhanced and comprehensive benchmark for compositional text-to-image generation

    Kaiyi Huang, Chengqi Duan, Kaiyue Sun, Enze Xie, Zhenguo Li, and Xihui Liu. T2i-compbench++: An enhanced and comprehensive benchmark for compositional text-to-image generation. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2025

    work page 2025

  9. [9]

    Tifa: Accurate and interpretable text-to-image faithfulness evaluation with question answering

    Yushi Hu, Benlin Liu, Jungo Kasai, Yizhong Wang, Mari Ostendorf, Ranjay Krishna, and Noah A Smith. Tifa: Accurate and interpretable text-to-image faithfulness evaluation with question answering. In ICCV , 2023

    work page 2023

  10. [10]

    T2i-compbench: A comprehensive benchmark for open-world compositional text-to-image generation

    Kaiyi Huang, Kaiyue Sun, Enze Xie, Zhenguo Li, and Xihui Liu. T2i-compbench: A comprehensive benchmark for open-world compositional text-to-image generation. In NeurIPS , volume 36, 2023

    work page 2023

  11. [11]

    Vectorfusion: Text-to-svg by abstracting pixel-based diffusion models

    Ajay Jain, Amber Xie, and Pieter Abbeel. Vectorfusion: Text-to-svg by abstracting pixel-based diffusion models. In CVPR , 2023

    work page 2023

  12. [12]

    A learned representation for scalable vector graphics

    Raphael Gontijo Lopes, David Ha, Douglas Eck, and Jonathon Shlens. A learned representation for scalable vector graphics. In ICCV , 2019

    work page 2019

  13. [13]

    Microsoft coco: Common objects in context

    Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll \'a r, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In ECCV . Springer, 2014

    work page 2014

  14. [14]

    Rlaif vs

    Harrison Lee, Samrat Phatale, Hassan Mansoor, Thomas Mesnard, Johan Ferret, Kellie Lu, Colton Bishop, Ethan Hall, Victor Carbune, Abhinav Rastogi, et al. Rlaif vs. rlhf: scaling reinforcement learning from human feedback with ai feedback. In ICML , 2024

    work page 2024

  15. [15]

    Coderl: Mastering code generation through pretrained models and deep reinforcement learning

    Hung Le, Yue Wang, Akhilesh Deepak Gotmare, Silvio Savarese, and Steven Chu Hong Hoi. Coderl: Mastering code generation through pretrained models and deep reinforcement learning. In NeurIPS , volume 35, 2022

    work page 2022

  16. [16]

    mplug: Effective and efficient vision-language learning by cross-modal skip-connections

    Chenliang Li, Haiyang Xu, Junfeng Tian, Wei Wang, Ming Yan, Bin Bi, Jiabo Ye, He Chen, Guohai Xu, Zheng Cao, et al. mplug: Effective and efficient vision-language learning by cross-modal skip-connections. In EMNLP , 2022

    work page 2022

  17. [17]

    Svgeditbench: A benchmark dataset for quantitative assessment of llm's svg editing capabilities

    Kunato Nishina and Yusuke Matsui. Svgeditbench: A benchmark dataset for quantitative assessment of llm's svg editing capabilities. In CVPR , 2024

    work page 2024

  18. [18]

    SVGEditBench V2: A Benchmark for Instruction-based SVG Editing

    Kunato Nishina and Yusuke Matsui. Svgeditbench v2: A benchmark for instruction-based svg editing. arXiv preprint arXiv:2502.19453 , 2025

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  19. [19]

    Codegen: An open large language model for code with multi-turn program synthesis

    Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong. Codegen: An open large language model for code with multi-turn program synthesis. arXiv preprint arXiv:2203.13474 , 2022

    Pith/arXiv arXiv 2022

  20. [20]

    Dinov2: Learning robust visual features without supervision

    Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Mahmoud Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Hervé Jegou, Julien Mairal, Patrick Lab...

    work page 2024

  21. [21]

    Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe. Training language models to follow instructions with human feedback....

    work page 2022

  22. [22]

    Neuralsvg: An implicit representation for text-to-vector generation

    Sagi Polaczek, Yuval Alaluf, Elad Richardson, Yael Vinker, and Daniel Cohen-Or. Neuralsvg: An implicit representation for text-to-vector generation. arXiv preprint arXiv:2501.03992 , 2025

    Pith/arXiv arXiv 2025

  23. [23]

    Can large language models understand symbolic graphics programs? In ICLR , 2025

    Zeju Qiu, Weiyang Liu, Haiwen Feng, Zhen Liu, Tim Z Xiao, Katherine M Collins, Joshua B Tenenbaum, Adrian Weller, Michael J Black, and Bernhard Sch \"o lkopf. Can large language models understand symbolic graphics programs? In ICLR , 2025

    work page 2025

  24. [24]

    Webrl: Training llm web agents via self-evolving online curriculum reinforcement learning

    Zehan Qi, Xiao Liu, Iat Long Iong, Hanyu Lai, Xueqiao Sun, Wenyi Zhao, Yu Yang, Xinyue Yang, Jiadai Sun, Shuntian Yao, et al. Webrl: Training llm web agents via self-evolving online curriculum reinforcement learning. arXiv preprint arXiv:2411.02337 , 2024

    Pith/arXiv arXiv 2024

  25. [25]

    Im2vec: Synthesizing vector graphics without vector supervision

    Pradyumna Reddy, Michael Gharbi, Michal Lukac, and Niloy J Mitra. Im2vec: Synthesizing vector graphics without vector supervision. In CVPR , 2021

    work page 2021

  26. [26]

    Learning transferable visual models from natural language supervision

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICML , 2021

    work page 2021

  27. [27]

    Starvector: Generating scalable vector graphics code from images and text

    Juan A Rodriguez, Abhay Puri, Shubham Agarwal, Issam H Laradji, Pau Rodriguez, Sai Rajeswar, David Vazquez, Christopher Pal, and Marco Pedersoli. Starvector: Generating scalable vector graphics code from images and text. In CVPR , 2025

    work page 2025

  28. [28]

    Challenging big-bench tasks and whether chain-of-thought can solve them

    Mirac Suzgun, Nathan Scales, Nathanael Sch \"a rli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V Le, Ed H Chi, Denny Zhou, , and Jason Wei. Challenging big-bench tasks and whether chain-of-thought can solve them. arXiv preprint arXiv:2210.09261 , 2022

    Pith/arXiv arXiv 2022

  29. [29]

    Proximal policy optimization algorithms

    John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 , 2017

    Pith/arXiv arXiv 2017

  30. [30]

    Deepseekmath: Pushing the limits of mathematical reasoning in open language models

    Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Yang Wu, et al. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300 , 2024

    Pith/arXiv arXiv 2024

  31. [31]

    Human preference score v2: A solid benchmark for evaluating human preferences of text-to-image synthesis

    Xiaoshi Wu, Yiming Hao, Keqiang Sun, Yixiong Chen, Feng Zhu, Rui Zhao, and Hongsheng Li. Human preference score v2: A solid benchmark for evaluating human preferences of text-to-image synthesis. arXiv preprint arXiv:2306.09341 , 2023

    Pith/arXiv arXiv 2023

  32. [32]

    Qwen-image technical report

    Chenfei Wu, Jiahao Li, Jingren Zhou, Junyang Lin, Kaiyuan Gao, Kun Yan, Sheng-ming Yin, Shuai Bai, Xiao Xu, Yilei Chen, et al. Qwen-image technical report. arXiv preprint arXiv:2508.02324 , 2025

    Pith/arXiv arXiv 2025

  33. [33]

    Chat2svg: Vector graphics generation with large language models and image diffusion models

    Ronghuan Wu, Wanchao Su, and Jing Liao. Chat2svg: Vector graphics generation with large language models and image diffusion models. In CVPR , 2025

    work page 2025

  34. [34]

    Iconshop: Text-guided vector icon synthesis with autoregressive transformers

    Ronghuan Wu, Wanchao Su, Kede Ma, and Jing Liao. Iconshop: Text-guided vector icon synthesis with autoregressive transformers. ACM Transactions on Graphics (TOG) , 42(6), 2023

    work page 2023

  35. [35]

    Better aligning text-to-image models with human preference

    Xiaoshi Wu, Keqiang Sun, Feng Zhu, Rui Zhao, and Hongsheng Li. Better aligning text-to-image models with human preference. arXiv preprint arXiv:2303.14420 , 1(3), 2023

    Pith/arXiv arXiv 2023

  36. [36]

    Webagent-r1: Training web agents via end-to-end multi-turn reinforcement learning

    Zhepei Wei, Wenlin Yao, Yao Liu, Weizhi Zhang, Qin Lu, Liang Qiu, Changlong Yu, Puyang Xu, Chao Zhang, Bing Yin, et al. Webagent-r1: Training web agents via end-to-end multi-turn reinforcement learning. arXiv preprint arXiv:2505.16421 , 2025

    arXiv 2025

  37. [37]

    Empowering llms to understand and generate complex vector graphics

    Ximing Xing, Juncheng Hu, Guotao Liang, Jing Zhang, Dong Xu, and Qian Yu. Empowering llms to understand and generate complex vector graphics. In CVPR , 2025

    work page 2025

  38. [38]

    Imagereward: Learning and evaluating human preferences for text-to-image generation

    Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong, Qinkai Li, Ming Ding, Jie Tang, and Yuxiao Dong. Imagereward: Learning and evaluating human preferences for text-to-image generation. Advances in Neural Information Processing Systems , 36:15903--15935, 2023

    work page 2023

  39. [39]

    Svgdreamer: Text guided svg generation with diffusion model

    Ximing Xing, Haitao Zhou, Chuang Wang, Jing Zhang, Dong Xu, and Qian Yu. Svgdreamer: Text guided svg generation with diffusion model. In CVPR , 2024

    work page 2024

  40. [40]

    Omnisvg: A unified scalable vector graphics generation model

    Yiying Yang, Wei Cheng, Sijin Chen, Xianfang Zeng, Fukun Yin, Jiaxu Zhang, Liao Wang, Gang Yu, Xingjun Ma, and Yu-Gang Jiang. Omnisvg: A unified scalable vector graphics generation model. arXiv preprint arXiv:2504.06263 , 2025

    arXiv 2025

  41. [41]

    Does reinforcement learning really incentivize reasoning capacity in llms beyond the base model? arXiv preprint arXiv:2504.13837 , 2025

    Yang Yue, Zhiqi Chen, Rui Lu, Andrew Zhao, Zhaokai Wang, Shiji Song, and Gao Huang. Does reinforcement learning really incentivize reasoning capacity in llms beyond the base model? arXiv preprint arXiv:2504.13837 , 2025

    Pith/arXiv arXiv 2025

  42. [42]

    Dapo: An open-source llm reinforcement learning system at scale

    Qiying Yu, Zheng Zhang, Ruofei Zhu, Yufeng Yuan, Xiaochen Zuo, Yu Yue, Weinan Dai, Tiantian Fan, Gaohong Liu, Lingjun Liu, et al. Dapo: An open-source llm reinforcement learning system at scale. arXiv preprint arXiv:2503.14476 , 2025

    Pith/arXiv arXiv 2025

  43. [43]

    Vgbench: Evaluating large language models on vector graphics understanding and generation

    Bocheng Zou, Mu Cai, Jianrui Zhang, and Yong Jae Lee. Vgbench: Evaluating large language models on vector graphics understanding and generation. arXiv preprint arXiv:2407.10972 , 2024

    Pith/arXiv arXiv 2024

  44. [44]

    Sigmoid loss for language image pre-training

    Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid loss for language image pre-training. In ICCV , 2023

    work page 2023

  45. [45]

    Fine-tuning language models from human preferences

    Daniel M Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving. Fine-tuning language models from human preferences. arXiv preprint arXiv:1909.08593 , 2019

    Pith/arXiv arXiv 1909

This paper was first reviewed by deepseek-v4-flash on August 5, 2026.