Replay Training

Replay training is a technique used to mitigate catastrophic forgetting in machine learning models, allowing them to learn from continuous streams of data without losing previously acquired knowledge. Current research focuses on improving the efficiency and effectiveness of replay, exploring methods like compressed memory representations and decoupled learning strategies to address memory limitations and biases towards newer data. These advancements are particularly relevant for continual learning in various domains, including image classification, relation extraction, and reinforcement learning for combinatorial optimization, enhancing model performance and sample efficiency in resource-constrained environments. The resulting improvements in sample efficiency and robustness have significant implications for deploying machine learning models in real-world applications where data is constantly evolving.