Fine Tuning Strategy

Fine-tuning strategies aim to adapt pre-trained models, such as large language models (LLMs) and convolutional neural networks (CNNs), to specific downstream tasks or domains using limited data. Current research focuses on optimizing various aspects of this process, including the choice of tunable parameters, supervision signals, and the integration of techniques like continuous pre-training, parameter-efficient methods (e.g., LoRA), and reinforcement learning. These advancements are crucial for improving the efficiency and effectiveness of model adaptation across diverse applications, ranging from medical image analysis and clinical decision support to robotics and natural language processing.