This article introduces Generative Adversarial Distillation (GAD), a method for training student models using only teacher-generated texts. Unlike traditional knowledge distillation, GAD employs a two-player game between a generator and a discriminator, enabling effective learning without probability supervision. The results demonstrate that models trained with GAD achieve performance comparable to their larger teacher models.