LLMs are advancing rapidly across multiple domains, yet their effectiveness in tackling complex financial problems remains an area of active investigation. The iterative development of LLMs has significantly driven the evolution of artificial intelligence toward artificial general intelligence (AGI). OpenAI’s o1 series and similar models like QwQ and Marco-o1 have improved complex reasoning capabilities by extending “chain-of-thought” reasoning through an iterative “exploration-reflection” approach. In finance, models such as XuanYuan-FinX1-Preview and Fino1 have showcased the potential of LLMs in cognitive reasoning tasks. Meanwhile, DeepSeekR1 adopts a different strategy, relying solely on RL with multi-stage training to enhance reasoning and inference abilities. By combining thousands of unsupervised RL training steps with a small cold-start dataset, DeepSeekR1 demonstrates strong emergent reasoning performance and readability, highlighting the effectiveness of RL-based methodologies in improving large-scale language models.
Despite these advancements, general-purpose LLMs struggle to adapt to specialized financial reasoning tasks. Financial decision-making requires interdisciplinary knowledge, including legal regulations, economic indicators, and mathematical modeling, while also demanding logical, step-by-step reasoning. Several challenges arise when deploying LLMs in financial applications. First, fragmented financial data complicates knowledge integration, leading to inconsistencies that hinder comprehensive understanding. Second, the black-box nature of LLMs makes their reasoning process difficult to interpret, conflicting with regulatory requirements for transparency and accountability. Finally, LLMs often struggle with generalization across financial scenarios, producing unreliable outputs in high-risk applications. These limitations pose significant barriers to their adoption in real-world financial systems, where accuracy and traceability are critical.
Researchers from Shanghai University of Finance & Economics, Fudan University, and FinStep have developed Fin-R1, a specialized LLM for financial reasoning. With a compact 7-billion-parameter architecture, Fin-R1 reduces deployment costs while addressing key economic challenges: fragmented data, lack of reasoning control, and weak generalization. It is trained on Fin-R1-Data, a high-quality dataset containing 60,091 CoT sourced from authoritative financial data. A two-stage training approach—Supervised Fine-Tuning (SFT) followed by RL—Fin-R1 enhances accuracy and interpretability. It performs well in financial benchmarks, excelling in financial compliance and robo-advisory applications.
The study presents a two-stage framework for constructing Fin-R1. The data generation phase involves creating a high-quality financial reasoning dataset, Fin-R1-Data, through data distillation with DeepSeek-R1 and filtering using an LLM-as-judge approach. In the model training phase, Fin-R1 is fine-tuned on Qwen2.5-7B-Instruct using SFT and Group Relative Policy Optimization (GRPO) to enhance reasoning and output consistency. The dataset combines open-source and proprietary financial data, refined through rigorous filtering. Training integrates supervised learning and reinforcement learning, incorporating structured prompts and reward mechanisms to improve financial reasoning accuracy and standardization.
The reasoning abilities of Fin-R1 in financial scenarios were evaluated through a comparative analysis against several state-of-the-art models, including DeepSeek-R1, Fin-R1-SFT, and various Qwen and Llama-based architectures. Despite its compact 7B parameter size, Fin-R1 achieved a notable average score of 75.2, ranking second overall. It outperformed all models of similar scale and exceeded DeepSeek-R1-Distill-Llama-70B by 8.7 points. Fin-R1 ranked highest in FinQA and ConvFinQA with scores of 76.0 and 85.0, respectively, demonstrating strong financial reasoning and cross-task generalization, particularly in benchmarks like Ant_Finance, TFNS, and Finance-Instruct-500K.
In conclusion, Fin-R1 is a large financial reasoning language model designed to tackle key challenges in financial AI, including fragmented data, inconsistent reasoning logic, and limited business generalization. It delivers state-of-the-art performance by utilizing a two-stage training process—SFT and RL—on the high-quality Fin-R1-Data dataset. With a compact 7B parameter scale, it achieves scores of 85.0 in ConvFinQA and 76.0 in FinQA, outperforming larger models. Future work aims to enhance financial multimodal capabilities, strengthen regulatory compliance, and expand real-world applications, driving innovation in fintech while ensuring efficient and intelligent financial decision-making.
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