Comment by rhdunn

The current approach of the big companies is to keep increasing the number of parameters (there was a report that recent Claude models are on the 4T parameter scale). Even recent SOTA open weight models are in the 1T-2T range.

This is partly due to the multiplicative scaling factor -- the number of tokens; the number of transformer layers; the number of features; etc.

I suspect that for models to have a step upgrade either/both of 1) a new/improved architecture model; and 2) better training of smaller parameter models are needed. I say the latter as I think that with the larger weights there is a lot of unused/redundant parameters.