Large Scale Pretraining

Large-scale pretraining leverages massive datasets to train foundational models that can then be fine-tuned for specific downstream tasks, significantly improving efficiency and performance compared to training from scratch. Current research focuses on optimizing pretraining strategies, including data curation techniques like deduplication and joint example selection, and exploring advanced architectures such as Vision Transformers and efficient adaptation methods like LoRA. This approach has yielded substantial improvements across diverse fields, from natural language processing and computer vision to drug discovery and remote sensing, by enabling high-performing models with reduced computational costs and data requirements.