this post was submitted on 25 Apr 2026
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If I understand it right you need to enrich and filter data with human input so as not to collapse the model.
Wouldn't that imply if the human enrichment is emulating AI data too closely it will still collapse the model, since it's now just the human filtering that's mimicing AI data?
The main mechanism leading to model collapse in that paper, as I understand it, are the loss of "rare" elements in the training data as each generation of model omits things that just don't happen to be asked of it. Like, if the original training data has just one single line somewhere that says "birds are nice", but the first generation of model never happens to be asked what it thinks of birds then this bit of information won't be present in the second generation. Over time the training data becomes homogenized. It probably also picks up an increasing load of false or idiosyncratic bits of information that were hallucinated and get reinforced due to random happenstance, it's been a long time since I read the article and details slip my mind.
I'm really not seeing how human filtering would mimic this process, so I think it's safe. The filtering is being done with intent in that case, not due to random drift as is done with a purely automated generation like was done in the paper.