[D] How researcher think of inductive bias when thinking of creating new/improving foundational models?

I am undergradute student learning machine learning.

What I got to know while reading few papers that we try to reduce search space by imposing inductive bias in machine learning models. And the success in creating useful models comes when inductive bias matches with the underlying data.

In heriarchical models like NVAE how they instilled inductive bias by specifing the way data gets computed? (I thinks it's called algorithmic bias, not sure though)

But how people think such inductive bias …

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