Continual Training

Continual training aims to enable machine learning models, particularly large language and vision models, to adapt to new data streams without catastrophic forgetting of previously learned information. Current research focuses on developing efficient algorithms and architectures, such as parameter-efficient fine-tuning methods and replay strategies, to address this challenge across various model types, including transformers and recurrent neural networks. This field is crucial for developing more sustainable and adaptable AI systems, improving their performance in dynamic real-world environments and reducing the environmental impact of frequent retraining.