Generalization Characteristic

Generalization, the ability of a model to perform well on unseen data, is a central challenge across machine learning domains. Current research focuses on understanding and improving generalization in diverse areas, including deep reinforcement learning, neural networks for image processing and vision-language tasks, and foundation models. Investigations explore factors like loss landscape characteristics, model architecture (e.g., ensemble methods, neuro-symbolic approaches), and the impact of training data characteristics (e.g., state space size, data diversity). These efforts aim to enhance model robustness and reliability in real-world applications, impacting fields ranging from robotics and computer vision to natural language processing.