Understanding Reinforcement Fine-Tuning (RFT)

Reinforcement fine-tuning (RFT) addresses the limitations of traditional supervised fine-tuning (SFT) by utilizing reinforcement learning methods to enhance model performance, particularly when data is scarce. While SFT focuses on aligning a model to specific labeled outputs, RFT emphasizes learning from qualitative feedback, allowing for more flexible task objectives. An application of RFT is found in code generation tasks, such as converting natural language to SQL, where there exists a clear right or wrong answer, facilitating incremental learning through performance grading. This shift towards RFT represents a merger of prior models in the landscape, enabling models to learn from both human preferences and objective tasks.