Fin-R1: A Large Language Model for Financial Reasoning through Reinforcement Learning
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arXiv
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| Format: | Preprint |
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2025
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| author | Liu, Zhaowei Guo, Xin Yang, Zhi Lou, Fangqi Zeng, Lingfeng Niu, Jinyi Li, Mengping Qi, Qi Liu, Zhiqiang Han, Yiyang Cheng, Dongpo Chen, Ronghao Wang, Huacan Feng, Xingdong Wang, Huixia Judy Shi, Chengchun Zhang, Liwen |
| author_facet | Liu, Zhaowei Guo, Xin Yang, Zhi Lou, Fangqi Zeng, Lingfeng Niu, Jinyi Li, Mengping Qi, Qi Liu, Zhiqiang Han, Yiyang Cheng, Dongpo Chen, Ronghao Wang, Huacan Feng, Xingdong Wang, Huixia Judy Shi, Chengchun Zhang, Liwen |
| contents | In recent years, general-purpose large language models (LLMs) such as GPT, Gemini, Claude, and DeepSeek have advanced at an unprecedented pace. Despite these achievements, their application to finance remains challenging, due to fragmented data sources, intransparent reasoning processes, and weak transferability to business applications. In response, we introduce Fin-R1, a reasoning LLM designed for financial scenarios. With a compact size of 7 billion parameters, Fin-R1 reduces deployment costs while addressing the aforementioned challenges. Its development follows a two-stage pipeline. First, we construct Fin-R1-Data, a high-quality financial dataset consisting of 60,091 chain-of-thought (CoT) samples, distilled and filtered from multiple authoritative benchmarks to ensure consistency and reliability. Second, we train Fin-R1 using Fin-R1-Data through supervised fine-tuning (SFT), followed by reinforcement learning (RL). This stage substantially improves the model's ability to solve complex financial reasoning tasks, yielding outputs that are both accurate and interpretable. Despite its relatively small parameter scale, Fin-R1 achieves competitive empirical performance across established financial benchmarks and demonstrates practical utility in compliance checking and robo-advisory. Our code is publicly available at https://github.com/SUFE-AIFLM-Lab/Fin-R1, and has already attracted over 700 stars. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_16252 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fin-R1: A Large Language Model for Financial Reasoning through Reinforcement Learning Liu, Zhaowei Guo, Xin Yang, Zhi Lou, Fangqi Zeng, Lingfeng Niu, Jinyi Li, Mengping Qi, Qi Liu, Zhiqiang Han, Yiyang Cheng, Dongpo Chen, Ronghao Wang, Huacan Feng, Xingdong Wang, Huixia Judy Shi, Chengchun Zhang, Liwen Computation and Language In recent years, general-purpose large language models (LLMs) such as GPT, Gemini, Claude, and DeepSeek have advanced at an unprecedented pace. Despite these achievements, their application to finance remains challenging, due to fragmented data sources, intransparent reasoning processes, and weak transferability to business applications. In response, we introduce Fin-R1, a reasoning LLM designed for financial scenarios. With a compact size of 7 billion parameters, Fin-R1 reduces deployment costs while addressing the aforementioned challenges. Its development follows a two-stage pipeline. First, we construct Fin-R1-Data, a high-quality financial dataset consisting of 60,091 chain-of-thought (CoT) samples, distilled and filtered from multiple authoritative benchmarks to ensure consistency and reliability. Second, we train Fin-R1 using Fin-R1-Data through supervised fine-tuning (SFT), followed by reinforcement learning (RL). This stage substantially improves the model's ability to solve complex financial reasoning tasks, yielding outputs that are both accurate and interpretable. Despite its relatively small parameter scale, Fin-R1 achieves competitive empirical performance across established financial benchmarks and demonstrates practical utility in compliance checking and robo-advisory. Our code is publicly available at https://github.com/SUFE-AIFLM-Lab/Fin-R1, and has already attracted over 700 stars. |
| title | Fin-R1: A Large Language Model for Financial Reasoning through Reinforcement Learning |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2503.16252 |