Adaptive Curriculum Strategies for Continuous Learning in Neural Networks

Abstract

Lifelong learning in neural networks aims to develop systems capable of adapting to new tasks and environments without forgetting previously acquired knowledge. A significant challenge in this domain is designing effective curricula that guide the learning process dynamically. This paper explores adaptive curriculum strategies that adjust learning pathways based on the evolving performance and needs of neural networks. We introduce a novel framework that integrates curriculum adaptation mechanisms, enabling continuous learning and improvement. By leveraging reinforcement learning and meta-learning techniques, our approach optimizes the sequence and complexity of training tasks, ensuring robust and sustained model performance over time. Empirical results across diverse benchmarks demonstrate that our adaptive curriculum strategies significantly enhance the adaptability and efficiency of neural networks, outperforming static curriculum methods and mitigating catastrophic forgetting. This work represents a pivotal step towards achieving robust continuous learning in artificial intelligence.

DOI - https://doi.org/10.65525/SVUP.9788199778009.2026.66-69