title: Fin-R1: A Large Language Model for Financial Reasoning through
    Reinforcement Learning
summary: 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.
paper_authors._id: 67dcc7b29c17514cb9815abd
paper_authors.name: Zhaowei Liu
paper_authors._id: 67dcc7b29c17514cb9815abe
paper_authors.name: Xin Guo
paper_authors._id: 67dcc7b29c17514cb9815abf
paper_authors.name: Fangqi Lou
paper_authors._id: 67dcc7b29c17514cb9815ac0
paper_authors.name: Lingfeng Zeng
paper_authors._id: 67dcc7b29c17514cb9815ac1
paper_authors.name: Jinyi Niu
paper_authors._id: 67dcc7b29c17514cb9815ac2
paper_authors.name: Zixuan Wang
paper_authors._id: 67dcc7b29c17514cb9815ac3
paper_authors.name: Jiajie Xu
paper_authors._id: 67dcc7b29c17514cb9815ac4
paper_authors.name: Weige Cai
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-25T08:21:24.666Z
paper_authors._id: 66221232d55e5f677e45e3c3
paper_authors.avatarUrl: /avatars/e944d999eba3eca8f441ad67ebab9e9f.svg
paper_authors.fullname: weego
paper_authors.type: user
paper_authors.user: caiweige
paper_authors._id: 67dcc7b29c17514cb9815ac5
paper_authors.name: Ziwei Yang
paper_authors._id: 67dcc7b29c17514cb9815ac6
paper_authors.name: Xueqian Zhao
paper_authors._id: 67dcc7b29c17514cb9815ac7
paper_authors.name: Chao Li
paper_authors._id: 67dcc7b29c17514cb9815ac8
paper_authors.name: Sheng Xu
paper_authors._id: 67dcc7b29c17514cb9815ac9
paper_authors.name: Dezhi Chen
paper_authors._id: 67dcc7b29c17514cb9815aca
paper_authors.name: Yun Chen
paper_authors._id: 67dcc7b29c17514cb9815acb
paper_authors.name: Zuo Bai
paper_authors._id: 67dcc7b29c17514cb9815acc
paper_authors.name: Liwen Zhang
paper_title: Fin-R1: A Large Language Model for Financial Reasoning through
    Reinforcement Learning
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: financial reasoning dataset DeepSeek-R1 supervised fine-tuning reinforcement learning FinQA ConvFinQA state-of-the-art