The speaker says that training on a changing, non-stationary data distribution can cause a model to forget older information.

“like it's definitely not like just like there isn't capacity like if you had some modeling with all this data and you take like literally the same size model and pre-train it from scratch with like all of this stuff in mid training it will be better and i think that's a lot of what's happening today and so it's very much like there's you know a bottleneck that stops us from just keeping training the same model forever versus just like getting all the data from the old model and like training a new model from scratch and this is exactly as jolly saying like some combination like plasticity and like catastrophic forgetting and that like that you know if you just naively”

“train on like non-stationary data because you're adding new data as you go basically this is messing with the data distribution so like the old stuff is just forgotten and”