Abstract: Unlike standard training, deep neural networks can suffer from serious overfitting problems in adversarial settings. Recent research [40,39] suggests that adversarial training can have nonvanishing generalization error even if the sample size $n$ goes to infinity. A natural question arises: can we eliminate the generalization error floor in adversarial training? This paper gives an affirmative answer. First, by an adaptation of information-theoretical lower bound on the complexity of solving Lipschitz-convex problems using randomized algorithms, we establish a minimax lower bound $\Omega(s(T)/n)$ given a training loss of $1/s(T)$ for the adversarial generalization gap, where $T$ is the number of iterations, and $s(T)\rightarrow+\infty$ as $T\rightarrow+\infty$. Next, by observing that the nonvanishing generalization error of existing adversarial training algorithms comes from the non-smoothness of the adversarial loss function, we employ a smoothing technique to smooth the adversarial loss function. Based on the smoothed loss function, we design a smoothed SGDmax algorithm achieving a generalization bound $\mathcal{O}(s(T)/n)$, which eliminates the generalization error floor and matches the minimax lower bound. Experimentally, we show that our algorithm improves adversarial generalization on common datasets.