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Pre-trained encoder-only and sequence-to-sequence (seq2seq) models each haveadvantages, however training both model types from scratch is computationallyexpensive. We explore recipes to improve pre-training efficiency byinitializing one model from the other. (1) Extracting the encoder from aseq2seq model, we show it under-performs a Masked Language Modeling (MLM)encoder, particularly on sequence labeling tasks. Variations of masking duringseq2seq training, reducing the decoder size, and continuing with a small amountof MLM training do not close the gap. (2) Conversely, using an encoder towarm-start seq2seq training, we show that by unfreezing the encoder partwaythrough training, we can match task performance of a from-scratch seq2seqmodel. Overall, this two-stage approach is an efficient recipe to obtain both amultilingual encoder and a seq2seq model, matching the performance of trainingeach model from scratch while reducing the total compute cost by 27%.

Recipes for Sequential Pre-training of Multilingual Encoder and Seq2Seq Models