# Scaling Text-Rich Image Understanding via Code-Guided Synthetic Multimodal Data Generation

arXiv: https://arxiv.org/abs/2502.14846 · HTML: https://mlsanity.com/paper/2502.14846/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 49 of its 76 references that are in the map.
- First posted on arXiv 20 Feb 2025.

## Placed in

- Multimodal LLMs / vision-language models / [MLLM training and visual instruction tuning](https://mlsanity.com/mllm/mllm-training-and-visual-instruction-tuning/)

## Builds on (most cited first, 30 of 49)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [LLaVA-OneVision: Easy Visual Task Transfer](https://mlsanity.com/paper/2408.03326/) (2024)
- [Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering](https://mlsanity.com/paper/1612.00837/) (2016)
- [Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering](https://mlsanity.com/paper/2209.09513/) (2022)
- [Towards VQA Models That Can Read](https://mlsanity.com/paper/1904.08920/) (2019)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning](https://mlsanity.com/paper/2203.10244/) (2022)
- [CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning](https://mlsanity.com/paper/1612.06890/) (2016)
- [DocVQA: A Dataset for VQA on Document Images](https://mlsanity.com/paper/2007.00398/) (2020)
- [OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments](https://mlsanity.com/paper/2404.07972/) (2024)
- [OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge](https://mlsanity.com/paper/1906.00067/) (2019)
- [VizWiz Grand Challenge: Answering Visual Questions from Blind People](https://mlsanity.com/paper/1802.08218/) (2018)
- [Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs](https://mlsanity.com/paper/2406.16860/) (2024)
- [A Diagram Is Worth A Dozen Images](https://mlsanity.com/paper/1603.07396/) (2016)
- [A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge](https://mlsanity.com/paper/2206.01718/) (2022)
- [CogAgent: A Visual Language Model for GUI Agents](https://mlsanity.com/paper/2312.08914/) (2023)
- [PaliGemma: A versatile 3B VLM for transfer](https://mlsanity.com/paper/2407.07726/) (2024)
- [Multimodal Few-Shot Learning with Frozen Language Models](https://mlsanity.com/paper/2106.13884/) (2021)
- [SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents](https://mlsanity.com/paper/2401.10935/) (2024)
- [Scaling Synthetic Data Creation with 1,000,000,000 Personas](https://mlsanity.com/paper/2406.20094/) (2024)
- [Hallucination of Multimodal Large Language Models: A Survey](https://mlsanity.com/paper/2404.18930/) (2024)
- [Navigating the Digital World as Humans Do: Universal Visual Grounding for GUI Agents](https://mlsanity.com/paper/2410.05243/) (2024)
- [Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning](https://mlsanity.com/paper/2209.14610/) (2022)
- [DVQA: Understanding Data Visualizations via Question Answering](https://mlsanity.com/paper/1801.08163/) (2018)
- [Scene Text Visual Question Answering](https://mlsanity.com/paper/1905.13648/) (2019)
- [PlotQA: Reasoning over Scientific Plots](https://mlsanity.com/paper/1909.00997/) (2019)

## Built on by (most cited first, 30 of 58)

- [Thinking with Images for Multimodal Reasoning: Foundations, Methods, and Future Frontiers](https://mlsanity.com/paper/2506.23918/) (2025)
- [FineVision: Open Data Is All You Need](https://mlsanity.com/paper/2510.17269/) (2025)
- [T2I-ReasonBench: Benchmarking Reasoning-Informed Text-to-Image Generation](https://mlsanity.com/paper/2508.17472/) (2025)
- [STEP3-VL-10B Technical Report](https://mlsanity.com/paper/2601.09668/) (2026)
- [VinciCoder: Unifying Multimodal Code Generation via Coarse-to-fine Visual Reinforcement Learning](https://mlsanity.com/paper/2511.00391/) (2025)
- [VisCoder: Fine-Tuning LLMs for Executable Python Visualization Code Generation](https://mlsanity.com/paper/2506.03930/) (2025)
- [MolmoPoint: Better Pointing for VLMs with Grounding Tokens](https://mlsanity.com/paper/2603.28069/) (2026)
- [DeepSketcher: Internalizing Visual Manipulation for Multimodal Reasoning](https://mlsanity.com/paper/2509.25866/) (2025)
- [BigCharts-R1: Enhanced Chart Reasoning with Visual Reinforcement Finetuning](https://mlsanity.com/paper/2508.09804/) (2025)
- [OCRVerse: Towards Holistic OCR in End-to-End Vision-Language Models](https://mlsanity.com/paper/2601.21639/) (2026)
- [DM0: An Embodied-Native Vision-Language-Action Model towards Physical AI](https://mlsanity.com/paper/2602.14974/) (2026)
- [ChartVerse: Scaling Chart Reasoning via Reliable Programmatic Synthesis from Scratch](https://mlsanity.com/paper/2601.13606/) (2026)
- [Phi-4-reasoning-vision-15B Technical Report](https://mlsanity.com/paper/2603.03975/) (2026)
- [VACoT: Rethinking Visual Data Augmentation with VLMs](https://mlsanity.com/paper/2512.02361/) (2025)
- [ChartGen: Scaling Chart Understanding Via Code-Guided Synthetic Chart Generation](https://mlsanity.com/paper/2507.19492/) (2025)
- [Scientific Image Synthesis: Benchmarking, Methodologies, and Downstream Utility](https://mlsanity.com/paper/2601.17027/) (2026)
- [ChartNet: A Million-Scale, High-Quality Multimodal Dataset for Robust Chart Understanding](https://mlsanity.com/paper/2603.27064/) (2026)
- [VisCoder2: Building Multi-Language Visualization Coding Agents](https://mlsanity.com/paper/2510.23642/) (2025)
- [Be My Eyes: Extending Large Language Models to New Modalities Through Multi-Agent Collaboration](https://mlsanity.com/paper/2511.19417/) (2025)
- [InteractScience: Programmatic and Visually-Grounded Evaluation of Interactive Scientific Demonstration Code Generation](https://mlsanity.com/paper/2510.09724/) (2025)
- [Enhancing Multi-Modal LLMs Reasoning via Difficulty-Aware Group Normalization](https://mlsanity.com/paper/2602.21743/) (2026)
- [Omni-I2C: A Holistic Benchmark for High-Fidelity Image-to-Code Generation](https://mlsanity.com/paper/2603.17508/) (2026)
- [OmniDiagram: Advancing Unified Diagram Code Generation via Visual Interrogation Reward](https://mlsanity.com/paper/2604.05514/) (2026)
- [MM-JudgeBias: A Benchmark for Evaluating Compositional Biases in MLLM-as-a-Judge](https://mlsanity.com/paper/2604.18164/) (2026)
- [Infinity-Parser2 Technical Report](https://mlsanity.com/paper/2607.07836/) (2026)
- [Self-Boosting Vision-Language Models with Noisy Student On-Policy Self-Distillation](https://mlsanity.com/paper/2607.23125/) (2026)
- [MindVL: Towards Efficient and Effective Training of Multimodal Large Language Models on Ascend NPUs](https://mlsanity.com/paper/2509.11662/) (2025)
- [Zone of Proximal Policy Optimization: Teacher in Prompts, Not Gradients](https://mlsanity.com/paper/2606.18216/) (2026)
- [Learning from Reasoning Failures via Synthetic Data Generation](https://mlsanity.com/paper/2504.14523/) (2025)
- [Prioritizing Image-Related Tokens Enhances Vision-Language Pre-Training](https://mlsanity.com/paper/2505.08971/) (2025)

## Abstract (arXiv)

Reasoning about images with rich text, such as charts and documents, is a critical application of vision-language models (VLMs). However, VLMs often struggle in these domains due to the scarcity of diverse text-rich vision-language data. To address this challenge, we present CoSyn, a framework that leverages the coding capabilities of text-only large language models (LLMs) to automatically create synthetic text-rich multimodal data. Given input text describing a target domain (e.g., "nutrition fact labels"), CoSyn prompts an LLM to generate code (Python, HTML, LaTeX, etc.) for rendering synthetic images. With the underlying code as textual representations of the synthetic images, CoSyn can generate high-quality instruction-tuning data, again relying on a text-only LLM. Using CoSyn, we constructed a dataset comprising 400K images and 2.7M rows of vision-language instruction-tuning data. Comprehensive experiments on seven benchmarks demonstrate that models trained on our synthetic data achieve state-of-the-art performance among competitive open-source models, including Llama 3.2, and surpass proprietary models such as GPT-4V and Gemini 1.5 Flash. Furthermore, CoSyn can produce synthetic pointing data, enabling VLMs to ground information within input images, showcasing its potential for developing multimodal agents capable of acting in real-world environments.