Fin-R1: A Large Language Model for Financial Reasoning through
  Reinforcement Learning
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.
67dcc7b29c17514cb9815abd
Zhaowei Liu
67dcc7b29c17514cb9815abe
Xin Guo
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Fangqi Lou
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Lingfeng Zeng
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Jinyi Niu
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Zixuan Wang
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Jiajie Xu
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Weige Cai
claimed_verified
2025-03-25T08:21:24.666Z
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weego
user
caiweige
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Ziwei Yang
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Xueqian Zhao
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Chao Li
67dcc7b29c17514cb9815ac8
Sheng Xu
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Dezhi Chen
67dcc7b29c17514cb9815aca
Yun Chen
67dcc7b29c17514cb9815acb
Zuo Bai
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Liwen Zhang
Fin-R1: A Large Language Model for Financial Reasoning through
  Reinforcement Learning
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.
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 dataset DeepSeek-R1 supervised fine-tuning reinforcement learning FinQA ConvFinQA state-of-the-art