[month13]

This is an older paper, but DeepMind alleges in their Chinchilla paper that far better performance can be extracted with fewer parameters; quote

"We find that current large language models are significantly under-trained, a consequence of the recent focus on scaling language models whilst keeping the amount of training data constant."

It's difficult to evaluate a LLM's performance as it's all qualitative, but Meta's LLaMA has been doing quite well, at even 13B parameters.

[astrange]

Chinchilla is aimed at finding a cost-performance tradeoff as well, not the optimal amount of training. If cost is no barrier because it'll be used forever, then probably there's no amount of training that's good enough.