In neural machine translation (NMT), sequence distillation (SD) through creation of distilled corpora leads to efficient (compact and fast) models.However, its effectiveness in extremely low-resource (ELR) settings has not been well-studied.On the other hand, transfer learning (TL) by leveraging larger helping corpora greatly improves translation quality in general.This paper investigates a combination of SD and TL for training efficient NMT models for ELR settings, where we utilize TL with helping corpora twice: once for distilling the ELR corpora and then during compact model training.We experimented with two ELR settings: Vietnamese-English and Hindi-English from the Asian Language Treebank dataset with 18k training sentence pairs.Using the compact models with 40% smaller parameters trained on the distilled ELR corpora, greedy search achieved 3.6 BLEU points improvement in average while reducing 40% of decoding time.We also confirmed that using both the distilled ELR and helping corpora in the second round of TL further improves translation quality.Our work highlights the importance of stage-wise application of SD and TL for efficient NMT modeling for ELR settings.