Fin-R1: A Large Language Model for Financial Reasoning through Reinforcement Learning
This work addresses the need for specialized AI tools in the financial sector, though it is incremental as it builds on existing methods like DeepSeek-R1.
The paper tackles the challenge of applying large language models to complex financial reasoning tasks by introducing Fin-R1, a model that achieves state-of-the-art performance on FinQA and ConvFinQA tasks and performs competitively with a 7-billion parameter size.
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.