# Why do machine learning engineers insist on training with more data than validation set?

Among my colleagues I have noticed a curious insistence on training with, say, 70% or 80% of data and validating on the remainder. The reason it is curious to me is the lack of any theoretical reasoning, and it smacks of influence from a five-fold cross-validation habit.

Is there any reason for choosing a larger training set when attempting to detect overfitting during training? In other words, why not use $$n^{0.75}$$ for training and $$n - n^{0.75}$$ for validation if the influence really is from cross-validation practices carried over from linear modeling theory as I suggest in this answer?

I posted a similar question on stats.stackexchange.com but based on the response thought I might have a more interesting discussion here. The concept of training for multiple epochs is, in my opinion, inherently Bayesian and thus the concept of cross-validation may be ill-suited at worst, unnecessary at best, for reasons I suggest in that post.