Fin-R1: A Large Language Model for Financial Reasoning through
  Reinforcement Learning
Authors: name: Zhaowei Liu, name: Xin Guo, name: Fangqi Lou, name: Lingfeng Zeng, name: Jinyi Niu, name: Zixuan Wang, name: Jiajie Xu, name: Weige Cai, name: Ziwei Yang, name: Xueqian Zhao, name: Chao Li, name: Sheng Xu, name: Dezhi Chen, name: Yun Chen, name: Zuo Bai, name: Liwen Zhang
Organization: AIFin Lab

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.