Abstract: Neural machine translation (NMT) models are usually trained using bilingual data. Building large-scale bilingual datasets is a huge challenge. In contrast, large-scale monolingual datasets for most languages are easier to construct. Pre-trained models (PTM) proposed in recent years can be trained on massive monolingual data. The generic representation of knowledge learned through pre-training helps achieve significant performance gains in downstream tasks. Currently pre-trained neural machine translation (PTNMT) has been extensively validated on resource-constrained datasets, but how to efficiently utilize PTM on high-resource NMT remains to be discussed. This paper focuses on reviewing and analyzing the current state and related problems of PTNMT, classifing PTNMT methods in terms of PTM's pre-trained methods, strategies, or specific tasks. We summarize the problems solved by PTNMT's methods, and conclude with a future outlook on PTNMT research.