Point-RFT: Improving Multimodal Reasoning with Visually Grounded Reinforcement Finetuning

Recent advances in large language models have significantly improved textual reasoning through the effective use of Chain-of-Thought (CoT) and reinforcement learning. However, extending these successes to vision-language tasks remains challenging due to inherent limitations in text-only CoT, such as visual hallucinations and insufficient multimodal integration. In this paper, we introduce Point-RFT, a multimodal reasoning framework explicitly designed to leverage visually grounded CoT reasoning for visual document understanding. Our approach consists of two stages: First, we conduct format finetuning using a curated dataset of 71K diverse visual reasoning problems, each annotated with detailed, step-by-step rationales explicitly grounded to corresponding visual elements. Second, we employ reinforcement finetuning targeting visual document understanding. On ChartQA, our approach improves accuracy from 70. 88% (format-finetuned baseline) to 90. 04%, surpassing the 83. 92% accuracy achieved by reinforcement finetuning relying solely on text-based CoT. The result shows that our grounded CoT is more effective for multimodal reasoning compared with the text-only CoT. Moreover, Point-RFT exhibits superior generalization capability across several out-of-domain visual document reasoning benchmarks, including CharXiv, PlotQA, IconQA, TabMWP, etc. , and highlights its potential in complex real-world scenarios.

Authors

• Minheng Ni Microsoft Research Asia
• Zhengyuan Yang University of Rochester
• Linjie Li
• Chung-Ching Lin
• Kevin Lin
• Wangmeng Zuo Harbin Institute of Technology
• Lijuan Wang

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