Optimal Adaptive Curriculum Generation for Continual Learning via Multi-Objective Variational Optimization

Abstract The increasing demand for intelligent systems capable of continual learning necessitates effective strategies for adaptive curriculum generation. In this paper, we introduce a novel optimization framework that addresses the dual objectives of maximizing forward transfer and minimizing catastrophic forgetting within resource constraints. We formulate the adaptive curriculum generation problem as a multi-objective variational optimization challenge, presenting an innovative algorithm that leverages gradient-based methods for efficient solution discovery. Our contributions include a comprehensive theoretical analysis that provides guarantees on generalization, convergence, and efficiency, alongside extensive empirical evaluations across standard benchmarks such as Split MNIST and CIFAR-100. Results demonstrate that our approach outperforms state-of-the-art methods, exhibiting superior forward transfer rates and reduced forgetting while optimizing resource utilization. By establishing new benchmarks for curriculum quality in the context of continual learning, we pave the way for future research and application of adaptive learning strategies across diverse domains. The findings underscore the importance of strategic curriculum design in enhancing learning outcomes and advancing autonomous AI systems.