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）以前所未有的速度发展。尽管取得了这些成就，其在金融领域的应用仍面临挑战，原因包括数据来源分散、推理过程不透明以及向商业应用的迁移能力较弱。为此，我们推出了Fin-R1，一种专为金融场景设计的推理大语言模型。Fin-R1具有70亿参数的紧凑规模，在降低部署成本的同时解决了上述挑战。其开发遵循两阶段流程。首先，我们构建了Fin-R1-Data，这是一个包含60,091个思维链样本的高质量金融数据集，通过从多个权威基准中蒸馏和筛选而成，以确保一致性和可靠性。其次，我们使用Fin-R1-Data通过监督微调（SFT）和随后的强化学习（RL）来训练Fin-R1。这一阶段显著提升了模型解决复杂金融推理任务的能力，产生既准确又可解释的输出。尽管参数规模相对较小，Fin-R1在现有金融基准测试中取得了具有竞争力的实证性能，并在合规检查和机器人投顾中展现了实际效用。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。