Normally is being strained here: yes, most finetuning isn't for things like this, but quite a substantial minority is for more accurate rendering of some narrow specific feature, especially ones typically identified as signature problems of AI image gen; publicly identifying this as a way to visually distinguish AI gens makes it more likely that fine tuning effort will be directed at addressing it.

I'm an engineer at an AI company that makes text/image to video models, but go off

I especially agree with the last sentence that models largely learn features in the dataset, but I don't understand why you would describe it as

> There's nobody that's like "today we're working on fingernails" or "today we're making hair physics work better"

If there were a business case for that, I would characterize curating a dataset of fingernails and fine-tuning or augmentind a model based on that as "today we're working on fingernails".

And the same too with eye reflections. So with the right dataset you can get eye reflections right, albeit in a limited domain. (E.g. deepfakes in a similar setting as the training data). In fact you can look at the community that sprung up around SD 1.5 (?) that fine tunes SD with relevant datasets to improve its abilities in exactly a "today we're going to improve its ability to produce these faces" kind of fashion.

Where did I misunderstand your comment? I seem to arrive at the completely opposite response from the same fact.

I also noticed that you say

> the improvements are really just giving the models techniques for more accurately replicating features from reasoning data.

You seem to refer to aspects of a model unrelated to dataset quality. But fine-tuning on a curated dataset may be sufficient and necessary for improving eye reflections and fingernails.