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

TL;DR

Point-RFT introduces a visually grounded CoT reasoning framework for multimodal understanding, significantly improving accuracy and generalization in visual document reasoning tasks by combining format finetuning and reinforcement finetuning.

Contribution

The paper presents a novel multimodal reasoning framework that explicitly grounds reasoning steps in visual elements, enhancing performance over text-only methods.

Findings

Achieved 90.04% accuracy on ChartQA, surpassing previous methods.

Demonstrated superior generalization across multiple out-of-domain benchmarks.

Grounded CoT outperforms text-only CoT in multimodal reasoning.

Abstract

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.