This paper proposes a method called persistent-negative adversarial distillation for improving black-box on-policy distillation in AI agents. The method addresses the moving-target problem by using historical, prompt-matched teacher-student comparisons to train a discriminator, which results in improved performance and smoother discriminator trajectories.