Simultaneous Training

Simultaneous training, a machine learning paradigm, involves training multiple models or components concurrently to improve efficiency and performance. Current research focuses on enhancing this approach through techniques like dual training for constrained model generation, asynchronous methods for federated learning, and integrating diverse optimizers (e.g., first- and second-order) within population-based training. These advancements address challenges such as biased data generation, slow convergence in distributed settings, and imbalanced task complexities, leading to improved model accuracy, robustness, and training speed across various applications including reinforcement learning, semi-supervised learning, and multi-modal data processing.