Multi Task Training

Multi-task training aims to improve the efficiency and generalization of machine learning models by training a single model to perform multiple related tasks simultaneously. Current research focuses on optimizing training strategies, including exploring the impact of optimization trajectories and developing methods like dynamic pipeline scheduling for efficient training of large models, as well as investigating the role of model architectures such as transformers and mixtures of experts. This approach offers significant potential for improving model performance, reducing computational costs, and enhancing the ability of models to generalize to new, unseen tasks across diverse domains, from natural language processing and speech recognition to computer vision and reinforcement learning.