ChartMaster: Advancing Chart-to-Code Generation with Real-World Charts and Chart Similarity Reinforcement Learning

• URL: http://arxiv.org/abs/2508.17608v2
• Date: Sun, 28 Sep 2025 06:18:42 GMT
• Title: ChartMaster: Advancing Chart-to-Code Generation with Real-World Charts and Chart Similarity Reinforcement Learning
• Authors: Wentao Tan, Qiong Cao, Chao Xue, Yibing Zhan, Changxing Ding, Xiaodong He,
• Abstract summary: The chart-to-code generation task requires MLLMs to convert chart images into executable code.<n>This task faces two main challenges: limited data diversity and the difficulty of maintaining visual consistency between generated charts and the original ones.<n>We propose ReChartPrompt, leveraging real-world, human-designed charts extracted from arXiv papers as prompts.<n>We also propose ChartSimRL, a GRPO-based reinforcement learning algorithm guided by a novel chart similarity reward.
• Score: 64.4193334712998
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The chart-to-code generation task requires MLLMs to convert chart images into executable code. This task faces two main challenges: limited data diversity and the difficulty of maintaining visual consistency between generated charts and the original ones. Existing datasets mainly rely on synthetic seed data to prompt GPT models for code generation, resulting in homogeneous samples that limit model generalization to real-world chart styles. To address this, we propose ReChartPrompt, leveraging real-world, human-designed charts extracted from arXiv papers as prompts. By harnessing the rich content and diverse visual styles of arXiv charts, we construct ReChartPrompt-240K, a large-scale and highly diverse dataset that better reflects realistic chart variations. For the second challenge, although SFT improves code understanding by optimizing next-token prediction, it does not provide direct supervision on visual features. As a result, it often fails to guarantee that the generated charts visually match the original ones. To address this, we propose ChartSimRL, a GRPO-based reinforcement learning algorithm guided by a novel chart similarity reward. This reward consists of two components: attribute similarity, which measures the overlap of chart attributes like layout and color between the generated and original charts, and visual similarity, which evaluates overall visual features, including texture, using convolutional neural networks. Unlike traditional text-based rewards, our reward accounts for the multimodal nature of the chart-to-code generation task, significantly enhancing the model's ability to accurately reproduce charts. Integrating ReChartPrompt and ChartSimRL, we develop the ChartMaster model, achieving SOTA results among 7B-parameter models and rivaling GPT-4o on various chart-to-code benchmarks. All resources are available at https://github.com/WentaoTan/ChartMaster.

Related papers

• BigCharts-R1: Enhanced Chart Reasoning with Visual Reinforcement Finetuning [51.472854950300416]
  We propose BigCharts, a dataset creation pipeline that generates visually diverse chart images.<n>Unlike purely synthetic datasets, BigCharts incorporates real-world data, ensuring authenticity and visual diversity.<n>By introducing novel reward signals specifically designed for chart reasoning, our approach enhances model robustness and generalization.
  arXiv  Detail & Related papers  (2025-08-13T13:39:17Z)
• ChartLens: Fine-grained Visual Attribution in Charts [106.44872805609673]
  Post-Hoc Visual Attribution for Charts identifies fine-grained chart elements that validate a given chart-associated response.<n>We propose ChartLens, a novel chart attribution algorithm that uses segmentation-based techniques to identify chart objects.<n>Our evaluations show that ChartLens improves fine-grained attributions by 26-66%.
  arXiv  Detail & Related papers  (2025-05-25T23:17:32Z)
• Socratic Chart: Cooperating Multiple Agents for Robust SVG Chart Understanding [14.75820681491341]
  Existing benchmarks reveal reliance on text-based shortcuts and probabilistic pattern-matching rather than genuine visual reasoning.<n>We propose Socratic Chart, a new framework that transforms chart images into Scalable Vector Graphics representations.<n>Our framework surpasses state-of-the-art models in accurately capturing chart primitives and improving reasoning performance.
  arXiv  Detail & Related papers  (2025-04-14T00:07:39Z)
• RefChartQA: Grounding Visual Answer on Chart Images through Instruction Tuning [63.599057862999]
  RefChartQA is a novel benchmark that integrates Chart Question Answering (ChartQA) with visual grounding.<n>Our experiments demonstrate that incorporating spatial awareness via grounding improves response accuracy by over 15%.
  arXiv  Detail & Related papers  (2025-03-29T15:50:08Z)
• Text2Chart31: Instruction Tuning for Chart Generation with Automatic Feedback [37.275533538711436]
  We propose a hierarchical pipeline and a new dataset for chart generation.<n>Our dataset, Text2Chart31, includes 31 unique plot types referring to the Matplotlib library.<n>We introduce a reinforcement learning-based instruction tuning technique for chart generation tasks without requiring human feedback.
  arXiv  Detail & Related papers  (2024-10-05T07:25:56Z)
• TinyChart: Efficient Chart Understanding with Visual Token Merging and Program-of-Thoughts Learning [83.58521787193293]
  We present TinyChart, an efficient MLLM for chart understanding with only 3B parameters. TinyChart overcomes two key challenges in efficient chart understanding: (1) reduce the burden of learning numerical computations through a Program-of-Thoughts (PoT) learning strategy, and (2) reduce lengthy vision feature sequences produced by the vision transformer for high-resolution images through a Vision Token Merging module.
  arXiv  Detail & Related papers  (2024-04-25T14:23:24Z)
• ChartAssisstant: A Universal Chart Multimodal Language Model via Chart-to-Table Pre-training and Multitask Instruction Tuning [54.89249749894061]
  ChartAssistant is a vision-language model for universal chart comprehension and reasoning. It undergoes a two-stage training process, starting with pre-training on chart-to-table parsing to align chart and text. Experimental results demonstrate significant performance gains over the state-of-the-art UniChart and Chartllama method.
  arXiv  Detail & Related papers  (2024-01-04T17:51:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.