Comment by dTal
19 hours ago
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.
The general case is that our own current relative ignorance on the best way to use and adapt pretrained weights is a short-lived anomaly caused by an abundance of funding to train models from scratch, a rapid evolution of training strategies and architectures, and a mad rush to ship hot new LLMs as fast as possible. But even as it is, the things you mentioned are not impossible, they are easy, and we are only going to get better at them.
>What if you need to reduce number of layers
Delete some.
> and/or width of hidden layers?
Randomly drop x% of parameters. No doubt there are better methods that entail distillation but this works.
> would the process of "layers to add" selection be considered training?
Er, no?
> What if you still have to obtain the best result possible for given coefficient/tokenization budget?
We don't know how to get "the best result possible", or even how to define such a thing. We only know how to throw compute at an existing network to get a "better" network, with diminishing returns. Re-using existing weights lowers the amount of compute you need to get to level X.
there is evidence it is useful in some cases, but obviously no evidence it is enough if you chase to beat SOTA.