Title: Revisiting Fast Adversarial Training. Abstract: Fast Adversarial Training (FAT) not only improves the model robustness but also reduces the training cost of standard adversarial training. However, FAT often suffers from Catastrophic Overfitting (CO), which results in poor robustness performance. CO describes the phenomenon that model robust accuracy can decrease dramatically and suddenly during the training of FAT. Many effective techniques have been developed to prevent CO and improve the model robustness from different perspectives. However, these techniques adopt inconsistent training settings and require different training costs, i.e, training time and memory costs, resulting in an unfair comparison. In this paper, we first conduct a comprehensive study of more than 10 FAT methods in terms of adversarial robustness and training costs. We revisit the effectiveness and efficiency of FAT techniques in preventing CO from the perspective of model local nonlinearity and propose an effective Lipschitz regularization method for FAT. Furthermore, we explore the effect of data augmentation and weight averaging in FAT and propose a simple yet effective auto weight averaging method to improve robustness further. By assembling these techniques, we propose a FGSM-based fast adversarial training method equipped with Lipschitz regularization and Auto Weight averaging, abbreviated as FGSM-LAW. Experimental evaluations on four benchmark databases demonstrate the superiority of the proposed method.