The emergent cross-lingual transfer seen in multilingual pretrained models has sparked significant interest in studying their behavior. However, because these analyses have focused on fully trained multilingual models, little is known about the dynamics of the multilingual pretraining process. We investigate when these models acquire their in-language and cross-lingual abilities by probing checkpoints taken from throughout XLM-R pretraining, using a suite of linguistic tasks. Our analysis shows that the model achieves high in-language performance early on, with lower-level linguistic skills acquired before more complex ones. In contrast, when the model learns to transfer cross-lingually depends on the language pair. Interestingly, we also observe that, across many languages and tasks, the final, converged model checkpoint exhibits significant performance degradation and that no one checkpoint performs best on all languages. Taken together with our other findings, these insights highlight the complexity and interconnectedness of multilingual pretraining.
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