Retraining Method

Retraining methods in machine learning aim to improve model performance or adapt them to new data without the computationally expensive process of training from scratch. Current research focuses on developing techniques for efficient retraining across various model architectures, including large language models and convolutional neural networks, often leveraging strategies like prompt recycling, sparse inference, and low-rank fine-tuning to minimize resource consumption. These advancements are crucial for enhancing the sustainability and scalability of AI applications, particularly in resource-constrained environments and domains requiring continuous adaptation to evolving data streams. The ability to rapidly and cost-effectively retrain models is vital for deploying and maintaining AI systems in dynamic real-world scenarios.