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PaperclipPerfector  7d ago (text post)   24932 thread views

Culture War Roundup for the week of October 5, 2026

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Even if it was a real problem

It is, but its not called Model Collapse, the specific technical phenomenon is called Mode Collapse. I have no idea if it is being accidentally confused here.

It can occur when synthetic data is less diverse than real data and outnumbers it. The model learns a narrower distribution, which occasionally cascades to mode collapse. Filtering isn't really a solution to deal with it, because the synthetic data is often being deliberately used. That might be a different problem than the LLM use-case assuming they aren't deliberately trying to train on other AI generated data, but I would doubt that if it starts happening its because they "accidentally" ingested too much AI-content vs a deliberate choice to economically generate synthetic data to perform better.

Are you sure? Or maybe I'm misreading your comment. Model collapse is the one where synthetic data swamps the real data but is mitigated by a real external reward signal. Mode collapse has to do with over use of RL.

Mode collapse has, to my recollection, nothing to do with RL. It was first discovered in GANs and has been a staple of generative AI since. It is however not a generational phenomenon, but I suppose it could be a longitudinal trend if each progressive model suffers more and more from it.

Maybe I could try and explain it better. Starting from the knowledge that human writing/art exists as a distribution, with a concentration around the mean and smaller tails. An AI model will tend to generate data closer to the mean, commonly called regression to the mean. If the AI generates too much synthetic data at the mean, and that data is combined with real data, it eventually will shift the mean, making the tails smaller, creating a feedback loop where each successive training run concentrates the data closer to the synthetic mean. At some point it ceases to express the full actual range of human arts/writing. This occurrence in the first model is called mode collapse, and as the models enter into the feedback loop, it gets worse.

The wiki definition isn't bad either:

In machine learning, mode collapse is a failure mode observed in generative models, originally noted in Generative Adversarial Networks (GANs). It occurs when the model produces outputs that are less diverse than expected, effectively "collapsing" to generate only a few modes of the data distribution while ignoring others. This phenomenon undermines the goal of generative models to capture the full diversity of the training data.

I don't think I disagree with this definition of mode collapse, but I'm pretty sure what the previous poster was describing was model collapse which is all about synthetic data.

In artificial intelligence, model collapse, also known as "AI inbreeding",[1][2] "AI cannibalism",[3][4] "Habsburg AI",[5] and "model autophagy disorder" or "MAD",[6][7][8] is the degradation of machine learning models from uncurated synthetic data, or from training on the outputs of another model, such as a prior versions of itself. It has colloquially been referred to as the AI version of mad cow disease,[8][9] fundamentally caused by feeding something to itself. It is unclear to what extent the model collapse threatens the long-term development of AI models, and techniques have been proposed to mitigate the effect.

I think you kind of need to stretch the definition of mode collapse to the breaking point for it to fit better than that.

Even when they first said model collapse I couldn't remember which it was but did look it up and I'm still pretty sure model collapse fits better here.

That's probably true, I think model collapse is the specific phenomenon that has been hypothesized. But I would also theorize that model collapse (2024) has inherited its concepts from mode collapse (2014). Mode Collapse is a far more general statistical property, it can occur without synthetic data, and it does not necessarily have to occur with synthetic data that has been forced to represent the full distribution. It just also very commonly occurs in synthetic data trained models.

The model collapse definition's additional nomenclature, gives non-rigorous, non-technical vibes.

That's what I was getting at, mea culpa.

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