Err, I suppose trivially, the higher rank terms include the lower-rank subnets, so they dominate in terms of quality.
But if you have some capacity constraint (e.g., memory, I guess?) then you can imagine dynamic rank allocation helping in the case where the maximum rank across all layers isn't within budget.
As someone else mentioned [0], the procedure would basically be to train a DyLoRA for an initial few iterations, then do a search among the layers to find the best scoring combination of ranks, and then train pruned to just use those ranks to completion.
Seems complicated but I could see it being useful potentially.