I do not know why would they do this when all labels are known, it could lead to wrong predictions being considered as pseudo labels, which would lead to further wrong predictions.
Maybe they set low priority in the beginning to pseudo labels, when pseudo labels might be incorrect, and with time, as accuracy increases on labeled dataset, give high priority to pseudo labels. But this sounds a bit confusing to me. There might be some other reason.