This paper describes the submission of LMU Munich to the WMT 2020\nunsupervised shared task, in two language directions, German<->Upper Sorbian.\nOur core unsupervised neural machine translation (UNMT) system follows the\nstrategy of Chronopoulou et al. (2020), using a monolingual pretrained language\ngeneration model (on German) and fine-tuning it on both German and Upper\nSorbian, before initializing a UNMT model, which is trained with online\nbacktranslation. Pseudo-parallel data obtained from an unsupervised statistical\nmachine translation (USMT) system is used to fine-tune the UNMT model. We also\napply BPE-Dropout to the low resource (Upper Sorbian) data to obtain a more\nrobust system. We additionally experiment with residual adapters and find them\nuseful in the Upper Sorbian->German direction. We explore sampling during\nbacktranslation and curriculum learning to use SMT translations in a more\nprincipled way. Finally, we ensemble our best-performing systems and reach a\nBLEU score of 32.4 on German->Upper Sorbian and 35.2 on Upper Sorbian->German.\n