In this podcast episode, we delve into Fin-R1, a groundbreaking large language model tailored for financial reasoning. Discover how this efficient 7 billion parameter model tackles critical financial industry challenges such as fragmented data, uncontrollable reasoning logic, and weak business generalization ability. We explore its innovative two-stage training framework, which includes the creation of the high-quality Fin-R1-Data dataset and the application of Supervised Fine-Tuning and Reinforcement Learning. Despite its compact size, Fin-R1 demonstrates outstanding performance on authoritative financial benchmarks, even surpassing larger models in certain tasks. Join us to learn about the potential of Fin-R1 in real-world financial applications like compliance and robo-advisory.