Oh that's neat and makes sense.
iiuc, basically when neural networks have enough neurons / pathways to encode a small amount of information, you have each neuron being used to encode a single 'feature', but as the amount of things it needs to encode for grows, it has to use individual neurons to encode multiple different things which entangles those concepts together.
So if you don't have toxic training data, then the general pattern of toxicity isn't very strong in the training data, so the concept of toxicity gets entangled with lots of other stuff, then when you tell the model to not be toxic it avoids a bunch of useful things.
If you instead feed it enough toxic data during training (but not too much), then the pattern of toxicity is more strongly isolated in the neuron encoding and less entangled with everything else, so when you tell it to not be toxic it doesn't impact everything else as much.