Dynamic Training

Dynamic training in machine learning focuses on adapting model architectures and training processes during learning, aiming to improve efficiency, performance, and adaptability. Current research explores this through various approaches, including training large models that can dynamically extract smaller subnetworks for different deployment needs, developing modular architectures with shared parameters for efficient training of multiple sub-models simultaneously, and employing adaptive training schedules for continual learning and reinforcement learning tasks. These advancements hold significant promise for reducing computational costs, enhancing model generalization across diverse tasks and resource constraints, and improving the robustness of AI systems in real-world applications.