Title: Towards Understanding Robust Memorization in Adversarial Training. Abstract: Adversarial training is a standard method to train neural networks to be robust to adversarial perturbation. However, in contrast with benign overfitting in the standard deep learning setting, which means that over-parameterized neural networks surprisingly generalize well for unseen data, while adversarial training method is able to achieve low robust training error, there still exists a significant robust generalization gap, which promotes us exploring what mechanism leads to robust overfitting during learning process. In this paper, we propose an implicit bias called $\textit{robust memorization}$ in adversarial training under the realistic data assumption. By function approximation theory, we prove that ReLU nets with efficient size have the ability to achieve robust memorization, while robust generalization requires exponentially large models. Then, we demonstrate robust memorization in adversarial training from both empirical and theoretical perspectives. In particular, we empirically investigate the dynamics of loss landscape over input, and we also provide theoretical analysis of robust memorization on data with linear separable assumption. Finally, we prove novel generalization bounds based on robust memorization, which further explains why deep neural networks have both high clean test accuracy and robust overfitting at the same time.