# SVGThinker: Instruction-Aligned and Reasoning-Driven Text-to-SVG Generation

arXiv: https://arxiv.org/abs/2509.24299 · HTML: https://mlsanity.com/paper/2509.24299/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 13 of its 37 references that are in the map.
- First posted on arXiv 29 Sep 2025.

## Placed in

- LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/)

## Builds on (most cited first, 13 of 13)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling](https://mlsanity.com/paper/2412.05271/) (2024)
- [LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://mlsanity.com/paper/2403.13372/) (2024)
- [Multimodal Chain-of-Thought Reasoning in Language Models](https://mlsanity.com/paper/2302.00923/) (2023)
- [StarVector: Generating Scalable Vector Graphics Code from Images and Text](https://mlsanity.com/paper/2312.11556/) (2023)
- [Empowering LLMs to Understand and Generate Complex Vector Graphics](https://mlsanity.com/paper/2412.11102/) (2024)
- [LayerTracer: Cognitive-Aligned Layered SVG Synthesis via Diffusion Transformer](https://mlsanity.com/paper/2502.01105/) (2025)
- [Chat2SVG: Vector Graphics Generation with Large Language Models and Image Diffusion Models](https://mlsanity.com/paper/2411.16602/) (2024)

## Built on by (most cited first, 8 of 8)

- [Render-in-the-Loop: Vector Graphics Generation via Visual Self-Feedback](https://mlsanity.com/paper/2604.20730/) (2026)
- [VAnim: Rendering-Aware Sparse State Modeling for Structure-Preserving Vector Animation](https://mlsanity.com/paper/2605.01517/) (2026)
- [IntroSVG: Learning from Rendering Feedback for Text-to-SVG Generation via an Introspective Generator-Critic Framework](https://mlsanity.com/paper/2603.09312/) (2026)
- [AmodalSVG: Amodal Image Vectorization via Semantic Layer Peeling](https://mlsanity.com/paper/2604.10940/) (2026)
- [SVGEval: A Vision-Grounded Framework for Perceptual-Quality Benchmarking and Evaluation in Text-to-SVG Generation](https://mlsanity.com/paper/2608.01977/) (2026)
- [ProxyImg: Towards Highly-Controllable Image Representation via Hierarchical Disentangled Proxy Embedding](https://mlsanity.com/paper/2602.01881/) (2026)
- [VecFontLLM: Anchor-Guided Direct Synthesis of Chinese Vector Fonts](https://mlsanity.com/paper/2607.17251/) (2026)
- [CANVAS: Consistency-Aware Navigation via Visual Adaptive Sampling for Long-Context Text-to-SVG Generation](https://mlsanity.com/paper/2608.30689/) (2026)

## Abstract (arXiv)

Scalable Vector Graphics (SVG) is a code-based representation for 2D visuals. Leveraging recent advances in large language models (LLMs), we study text-to-SVG generation and address two persistent gaps: weak generalization and poor adherence to input instructions. We present SVGThinker, a reasoning-driven framework that aligns the production of SVG code with the visualization process and supports the full set of SVG primitives. Our pipeline first renders each primitive in sequence and uses a multimodal model to annotate the image and code; we then build stepwise updates that mirror the incremental addition of primitives. On this data, we train an LLM with supervised fine-tuning that exposes its chain-of-thought as intermediate reasoning, improving robustness and reducing errors and hallucinations. Experiments against state-of-the-art baselines show that SVGThinker produces more stable, editable, and higher-quality SVGs while preserving the structural advantages of vector graphics. Unlike image-based methods, our outputs enable precise and hierarchical editing, opening new directions for design, content creation, and automated graphics generation.