Lightweight Continual

Lightweight continual learning focuses on developing efficient machine learning models that can incrementally acquire new knowledge without catastrophic forgetting or excessive computational demands. Current research emphasizes techniques like module composition and pruning, knowledge fusion of pre-trained models, and memory-efficient replay methods, often applied to large language models and spiking neural networks. These advancements are significant because they enable the development of more adaptable and resource-friendly AI systems for various applications, ranging from natural language processing and automated reasoning to environmental prediction. The ultimate goal is to create AI that can continuously learn and adapt in real-world scenarios with limited computational resources.