Fine-tuning and Preference Alignment in a Single Streamlined Process

Jiwoo Hong and Noah Lee from KAIST AI discuss their method ORPO, combining supervised fine-tuning and preference alignment in a single step. They highlight the advantages of their approach, such as minimal data requirement, bias prevention, and enhanced adaptability of language models. The Orpo method has received positive feedback from the research community and industry for efficient alignment and scaling models with smaller datasets.