I think this is a valuable contribution. I used to think that Demix-like techniques would dominate in this space because in principle they could achieve close-to-zero alignment tax, but actually absorption is probably crucial, especially in large pre-training runs where models might learn with very limited mislabeled data.
I am unsure whether techniques like gradient routing can ever impose a <10x alignment tax, but I think like a lot can be done here (e.g. by combining Demix and gradient routing, or maybe by doing something more clean, though I don’t know what that would look like), and I would not be shocked if techniques that descend from gradient routing became essential components of 2030-safety.
I think this is a valuable contribution. I used to think that Demix-like techniques would dominate in this space because in principle they could achieve close-to-zero alignment tax, but actually absorption is probably crucial, especially in large pre-training runs where models might learn with very limited mislabeled data.
I am unsure whether techniques like gradient routing can ever impose a <10x alignment tax, but I think like a lot can be done here (e.g. by combining Demix and gradient routing, or maybe by doing something more clean, though I don’t know what that would look like), and I would not be shocked if techniques that descend from gradient routing became essential components of 2030-safety.