Transformer Training

Transformer training focuses on optimizing the performance and efficiency of transformer-based models, primarily through advancements in algorithms and hardware. Current research emphasizes improving training stability, addressing the quadratic complexity of self-attention (e.g., through linear attention mechanisms), and developing efficient training strategies for long sequences and large datasets. These efforts are crucial for expanding the applicability of transformers to increasingly complex tasks and resource-constrained environments, impacting fields ranging from natural language processing and computer vision to scientific modeling and forecasting.