FinLMM-R1: Enhancing Financial Reasoning in LMM through Scalable Data and Reward Design

TL;DR

FinLMM-R1 introduces a scalable data construction pipeline and enhanced training strategies to significantly improve financial reasoning capabilities in large multimodal models, addressing data scarcity and training inefficiencies.

Contribution

The paper presents FinLMM-R1, a novel framework combining an automated data pipeline with adversarial reward-based training to enhance multimodal reasoning in financial LMMs.

Findings

Improved answer accuracy on 7 benchmarks.

Enhanced reasoning depth in financial multimodal tasks.

Effective handling of textual-visual misalignment.

Abstract

Large Multimodal Models (LMMs) demonstrate significant cross-modal reasoning capabilities. However, financial applications face challenges due to the lack of high-quality multimodal reasoning datasets and the inefficiency of existing training paradigms for reasoning enhancement. To address these issues, we propose an integrated framework, FinLMM-R1, combining an automated and scalable pipeline for data construction with enhanced training strategies to improve the multimodal reasoning of LMM. The Automated and Scalable Pipeline (ASP) resolves textual-visual misalignment in financial reports through a separate paradigm of question-answer generation and image-question alignment, ensuring data integrity and extraction efficiency. Through ASP, we collect 89,378 aligned image-question pairs from 23,397 financial reports, covering tasks such as arithmetic reasoning, statistics reasoning, financial explanation, and financial knowledge. Moreover, we introduce the Thinking with Adversarial Reward in LMM (TAR-LMM), extending the prior two-stage training framework [1] with additional reward mechanisms. In the first stage, we focus on text-only tasks with format and accuracy rewards to guide the model in generating well-structured thinking contents. In the second stage, we construct multi-image contrastive samples with additional reward components including image selection, thinking content length, and adversarial reward to jointly optimize the LMM across visual perception, reasoning efficiency, and logical coherence. Extensive experiments on 7 benchmarks show ASP-derived dataset and training framework significantly improve answer accuracy and reasoning depth over existing reasoning LMMs in both general and financial multimodal contexts.