Differentiable optimization-based algorithms, such as MAML, OptNet, and MGRL, have flourished recently. Meta-gradient, or the gradient term of outer-loop variables obtained by differentiating through the inner-loop optimization process, is one of the crucial components of differentiable optimization. Machine learning models can improve the sampling efficiency and the ultimate performance by utilizing meta-gradients. There are various difficulties in creating differentiable optimization algorithms. Before implementing algorithms with gradient flows on complex computational graphs, developers must realize different inner-loop optimization techniques. Examples include Gumbel-Softmax to differentiate through discrete distribution, function interpolation for differentiable combinatorial solvers, explicit gradient computation of unrolled optimization, implicit gradient
