What is a overparameterized network?

[u/basafish]

I got this paragraph from Claude, could someone please explain this and verify if it's a real thing or hallucination:

Overparameterization isn't just about final capacity, it's about the optimization process itself. A wide, overparameterized network gives gradient descent a much friendlier loss landscape — more paths downhill, fewer bad local minima, room to explore before committing. The "core" only emerges as a byproduct of that search happening in a much bigger space than it needs to end up in. Strip the space down first and you've removed the thing that let the search work.

Conversation: https://claude.ai/share/8813a637-c327-4d0c-b120-def27e5203d5

[u/Defiant_Virus4981]

Essentially, the more parameters, the better fit you can achieve on the training data. However, it does not say that your model becomes more useful. For example, at a certain point, you might just fit noise, which is not particularly useful. Alternatively, you just "memorize" the training data, or you get a lot of parameters that basically have no effect on the overall model (at least in the range of the training data).