Zeroth-Order Optimizers¶
Zeroth-order (gradient-free) methods train models using only function evaluations, estimating gradients from randomized perturbations of the parameters instead of backpropagation. Because they need no backward pass or activation storage, they run at roughly inference-level memory, which has made them a practical option for fine-tuning large language models on constrained hardware. The lineage runs from SPSA in classical stochastic approximation to recent variance-reduced and low-rank variants built on MeZO.