Low Training Error

Low training error, while seemingly desirable, doesn't guarantee good generalization in machine learning models. Current research focuses on understanding the discrepancy between low training error and poor performance on unseen data, exploring factors like distributional shifts in reinforcement learning and the impact of model architecture and training dynamics on generalization. This research is crucial for improving model robustness and reliability, particularly in applications where high accuracy on unseen data is paramount, such as medical diagnosis or autonomous systems. Addressing this "error-generalization gap" is a key challenge driving advancements in model design and training methodologies.