I certainly don't think it will help at all with stability. Things like Q/K layernorm are better tricks for softmax stability when scaling: https://arxiv.org/pdf/2302.05442.pdf
How would you have known if the trick actually reduces the outliers in the weights? Even if the transformer quality does not improve overall, having less outliers as a result is very beneficial for more accurate quantization of the data
The "how" is pretty straightforward.
>> because no one has yet looked at whether the trick helps reducing outliers in very large models
Given a softmax version doing exactly as the blog post says is baked into a google library (see this thread), and you can set it as a parameter in a pytorch model (see this thread), this claim seems off. "Let's try X, oh, X doesn't do much, let's not write a paper about it" is extremely common for many X.
Agree - the "how" is straightforward