Title: Zipper: Decoupling the tradeoff Between Robustness and Accuracy. Abstract: Deep neural networks obtained by standard training have been constantly plagued by adversarial examples. Although adversarial training demonstrates its capability to defend against adversarial examples, unfortunately, training robust classifiers leads to an inevitable drop in the natural generalization when performing adversarial training. To address the issues, we decouple the standard generalization and the robust generalization from joint training and formulate different training strategies for each one. Specifically, instead of minimizing a global loss on the expectation over these two generalization errors, we propose a bi-expert framework called \emph{Zipper} where we simultaneously train base learners with distribution-aware strategies so that they can specialize in their own fields. The parameters of base learners are collected and combined to form a global learner at intervals during the training process, which is then distributed to base learners as initialized parameters for continued training. Theoretically, we show that the risks of Zipper will get lower once the base learners are well trained. Extensive experiments verify the applicability of Zipper to satisfying high clean accuracy in the natural setting while keeping considerably robust to the adversarial setting, compared to relevant techniques. 