None of that is true, at least in theory. You can trivially change layer size simply by adding extra columns initialized as 0, effectively embedding your smaller network in a larger network. You can add layers in a similar way, and in fact LLMs are surprisingly robust to having layers added and removed - you can sometimes actually improve performance simply by duplicating some middle layers[0]. Tokenization is probably the hardest but all the layers between the first and last just encode embeddings; it's probably not impossible to retrain those while preserving the middle parts.

[0] https://news.ycombinator.com/item?id=47431671 https://news.ycombinator.com/item?id=47322887

You took a simple path, embedding smaller into larger. What if you need to reduce number of layers and/or width of hidden layers? How will you embed larger into smaller? As for the "addition of same layers" - would the process of "layers to add" selection be considered training?

What if you still have to obtain the best result possible for given coefficient/tokenization budget?

I think that my comment express general case, while yours provide some exceptions.

there is evidence it is useful in some cases, but obviously no evidence it is enough if you chase to beat SOTA.