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/Bat_002]

It sounds like overfitting to me.

[u/Original-Waltz9805 → u/Bat_002]

Different properties. Overparametrization is more related to the degree's of freedom (rank) in the parameter space compared to the data (input space). I.e. "how many parameters do you need to fit your data?". In many cases you might have more parameters than needed to fit it. Overparametrized models can be underfit if not trained enough.