One potential limitation or drawback of employing distilling neural architecture techniques in NAS is the reliance on a teacher model for supervision during training. While knowledge distillation can be effective in transferring knowledge from a teacher network to a student network, it may introduce biases based on the architecture and performance of the teacher model. This could potentially limit the diversity of architectures discovered through NAS and lead to suboptimal solutions if not carefully managed. Additionally, there may be challenges in ensuring that distilled knowledge accurately captures essential architectural features without introducing unnecessary constraints or biases.