{"ID":2882809,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.09804","arxiv_id":"2508.09804","title":"BigCharts-R1: Enhanced Chart Reasoning with Visual Reinforcement Finetuning","abstract":"Charts are essential to data analysis, transforming raw data into clear visual representations that support human decision-making. Although current vision-language models (VLMs) have made significant progress, they continue to struggle with chart comprehension due to training on datasets that lack diversity and real-world authenticity, or on automatically extracted underlying data tables of charts, which can contain numerous estimation errors. Furthermore, existing models only rely on supervised fine-tuning using these low-quality datasets, severely limiting their effectiveness. To address these issues, we first propose BigCharts, a dataset creation pipeline that generates visually diverse chart images by conditioning the rendering process on real-world charts sourced from multiple online platforms. Unlike purely synthetic datasets, BigCharts incorporates real-world data, ensuring authenticity and visual diversity, while still retaining accurate underlying data due to our proposed replotting process. Additionally, we introduce a comprehensive training framework that integrates supervised fine-tuning with Group Relative Policy Optimization (GRPO)-based reinforcement learning. By introducing novel reward signals specifically designed for chart reasoning, our approach enhances model robustness and generalization across diverse chart styles and domains, resulting in a state-of-the-art chart reasoning model, BigCharts-R1. Extensive experiments demonstrate that our models surpass existing methods on multiple chart question-answering benchmarks compared to even larger open-source and closed-source models.","short_abstract":"Charts are essential to data analysis, transforming raw data into clear visual representations that support human decision-making. Although current vision-language models (VLMs) have made significant progress, they continue to struggle with chart comprehension due to training on datasets that lack diversity and real-wo...","url_abs":"https://arxiv.org/abs/2508.09804","url_pdf":"https://arxiv.org/pdf/2508.09804v1","authors":"[\"Ahmed Masry\",\"Abhay Puri\",\"Masoud Hashemi\",\"Juan A. Rodriguez\",\"Megh Thakkar\",\"Khyati Mahajan\",\"Vikas Yadav\",\"Sathwik Tejaswi Madhusudhan\",\"Alexandre Piché\",\"Dzmitry Bahdanau\",\"Christopher Pal\",\"David Vazquez\",\"Enamul Hoque\",\"Perouz Taslakian\",\"Sai Rajeswar\",\"Spandana Gella\"]","published":"2025-08-13T13:39:17Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[\"Reinforcement Learning\",\"Language Model\"]","has_code":false}