Pre Training

Pre-training involves initially training large models on massive datasets to learn generalizable features before fine-tuning them for specific tasks. Current research focuses on improving data efficiency through techniques like carefully curated datasets, task-oriented pre-training, and novel data selection methods, often employing transformer architectures and contrastive learning. These advancements aim to reduce computational costs and enhance model performance across diverse domains, impacting fields ranging from natural language processing and computer vision to medical imaging and graph analysis. The ultimate goal is to create more robust, efficient, and adaptable models with reduced environmental impact.