The bottleneck is the total compute budget devoted to training, so while I'm quite certain that stacking a few more layers can be done and would have some benefit, it might well be that spending the same extra compute on a larger context window or 'wider' layers or simply doing more iterations on the same data would have a larger benefit than more layers, and if the people training the very large models think so, they would do these other things instead of stacking more layers.
Brudaks
joined 10 months ago
Cross-validation is a reasonable alternative, however, it does increase your compute cost 5-10 times, or, more likely, means that you generate 5-10 times smaller model(s) which are worse than you could have made if you'd just made a single one.