Memory-Efficient Gradient Unrolling for Large-Scale Bi-level Optimization

Bi-level optimizaiton (BO) has become a fundamental mathematical framework for addressing hierarchical machine learning problems.As deep learning models continue to grow in size, the demand for scalable bi-level optimization has become increasingly critical.Traditional gradient-based bi-level optimizaiton algorithms, due to their inherent characteristics, are ill-suited to meet the demands of large-scale applications.In this paper, we introduce **F**orward **G**radient **U**nrolling with **F**orward **G**radient, abbreviated as **$($FG$)^2$U**, which achieves an unbiased stochastic approximation of the meta gradient for bi-level optimizaiton.$($FG$)^2$U