Training Recipe

"Training recipes" in machine learning encompass the optimization strategies and data handling techniques used to effectively train complex models. Current research focuses on improving efficiency and performance across diverse model architectures, including transformers and conformers, by exploring techniques like synthetic data generation, novel loss functions, and structured sparsity. These advancements are crucial for scaling model capabilities, particularly in resource-intensive applications such as long-context language modeling, weather prediction, and brain encoding, ultimately leading to more accurate and efficient AI systems.