Module Composition

Module composition focuses on building complex systems or models from smaller, reusable components, aiming to improve efficiency, generalization, and adaptability. Current research explores hierarchical architectures, often combining imitation and reinforcement learning, and leverages techniques like adaptive pruning and dynamic module selection to optimize resource usage and handle continual learning scenarios. This approach holds significant promise for advancing robotics, natural language processing, and other fields by enabling more efficient and robust systems capable of handling diverse and evolving tasks.