Offline Model

Offline models leverage pre-collected data to train machine learning models, aiming to improve efficiency and reduce the need for real-time data acquisition. Current research focuses on enhancing offline model robustness (e.g., addressing proxy model misspecification in reinforcement learning), optimizing algorithm selection for computationally expensive tasks, and developing efficient data selection techniques to reduce training data size while maintaining accuracy. These advancements are significant for various applications, including biological sequence design, autonomous systems, and federated learning, by enabling more efficient and effective model training in resource-constrained or data-heterogeneous environments.