The speaker says that in reinforcement learning, retaining uncertainty rather than using only a point estimate can be one of the best ways to improve algorithm performance through better exploration.

“Doing inference at some point, what you lose is you lose kind of the the breadth of the density. But that's more an assumption, right? You can still do exactly the same way you're currently doing, but get an entire mean and standard deviation. And that can be super useful. Like let's say uh like my background is reinforcement learning. In reinforcement learning, one of the best things you can do to actually improve the performance of your algorithm is to say I don't actually want a point estimate.”