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 atthis https URL.
View on arXiv@article{liu2025_2503.16252,
title={ Fin-R1: A Large Language Model for Financial Reasoning through Reinforcement Learning },
author={ Zhaowei Liu and Xin Guo and Fangqi Lou and Lingfeng Zeng and Jinyi Niu and Zixuan Wang and Jiajie Xu and Weige Cai and Ziwei Yang and Xueqian Zhao and Chao Li and Sheng Xu and Dezhi Chen and Yun Chen and Zuo Bai and Liwen Zhang },
journal={arXiv preprint arXiv:2503.16252},
year={ 2025 }
}