Dynamic Fine-Tuning (DFT): Bridging the Generalization Gap in Supervised Fine-Tuning (SFT) for LLMs

The article introduces Dynamic Fine-Tuning (DFT), a novel approach designed to enhance the generalization capabilities of Supervised Fine-Tuning (SFT) in large language models (LLMs). While SFT is efficient for task adaptation using expert demonstration datasets, it often struggles with generalization compared to reinforcement learning (RL), which explores diverse strategies but at a higher computational cost. DFT aims to bridge this gap by dynamically integrating elements of RL into the SFT process, potentially enabling models to achieve better generalization without the extensive resource demands associated with pure RL methods. This development addresses a critical challenge