Fin-R1: A Large Language Model for Financial Reasoning through
  Reinforcement Learning

Zhaowei Liu; Xin Guo; Fangqi Lou; Lingfeng Zeng; Jinyi Niu; Zixuan Wang; Jiajie Xu; Weige Cai; Ziwei Yang; Xueqian Zhao; Chao Li; Sheng Xu; Dezhi Chen; Yun Chen; Zuo Bai; Liwen Zhang

Paper · arXiv 2503.16252

Fin-R1: A Large Language Model for Financial Reasoning through Reinforcement Learning

Zhaowei Liu, Xin Guo, Fangqi Lou, Lingfeng Zeng, Jinyi Niu, Zixuan Wang, Jiajie Xu, Weige Cai, Ziwei Yang, Xueqian Zhao, Chao Li, Sheng Xu, Dezhi Chen, Yun Chen, Zuo Bai, Liwen Zhang

32 upvotesMarch 20, 2025arXiv 预印本

AI 摘要

Fin-R1, a large language model tailored for finance, achieves state-of-the-art performance in financial reasoning tasks using supervised fine-tuning and reinforcement learning.

financial reasoning datasetDeepSeek-R1supervised fine-tuningreinforcement learningFinQAConvFinQAstate-of-the-art

Abstract

Reasoning large language models are rapidly evolving across various domains. However, their capabilities in handling complex financial tasks still require in-depth exploration. In this paper, we introduce Fin-R1, a reasoning large language model specifically designed for the financial sector. Fin-R1 is built using a two-stage architecture, leveraging a financial reasoning dataset distilled and processed based on DeepSeek-R1. Through supervised fine-tuning (SFT) and reinforcement learning (RL) training, it demonstrates performance close to DeepSeek-R1 with a parameter size of 7 billion across a range of financial reasoning tasks. It achieves the state-of-the-art (SOTA) in the FinQA and ConvFinQA tasks between those LLMs in our evaluation, surpassing larger models in other tasks as well. Fin-R1 showcases strong reasoning and decision-making capabilities, providing solutions to various problems encountered in the financial domain. Our code is available at https://github.com/SUFE-AIFLM-Lab/Fin-R1.