Reinforcement Fine-Tuning Revolution

This chapter explores the shift from supervised fine-tuning (SFT) to reinforcement fine-tuning (RFT) in AI, addressing the limitations of traditional methods. It highlights the importance of high-quality labeled data and introduces RFT as a more efficient alternative that allows for creative exploration and better generalization. The discussion includes practical coding examples and the advantages of RFT in scenarios lacking ground truth data, showcasing its potential for improved model performance.