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.

翻译：近年来，通用大语言模型（如GPT、Gemini、Claude和DeepSeek）以前所未有的速度取得进展。尽管取得了这些成就，但由于数据源碎片化、推理过程不透明以及向商业应用迁移能力较弱，它们在金融领域的应用仍面临挑战。为此，我们提出Fin-R1，一个专为金融场景设计的推理大语言模型。该模型参数规模精简至70亿，在降低部署成本的同时，有效应对上述挑战。其开发遵循两阶段流程：首先，我们构建了Fin-R1-Data高质量金融数据集，包含60,091个思维链样本，这些样本从多个权威基准中蒸馏并过滤，确保了数据的一致性与可靠性；其次，我们利用Fin-R1-Data通过监督微调和后续强化学习对Fin-R1进行训练。这一阶段显著提升了模型解决复杂金融推理任务的能力，使其输出兼具准确性与可解释性。尽管参数规模相对较小，Fin-R1在已有金融基准测试中展现出具有竞争力的实证性能，并在合规检查与智能投顾领域证实了其实用价值。我们的代码已在https://github.com/SUFE-AIFLM-Lab/Fin-R1公开，并已获得超过700颗星标。