I found an effect that explains this.
LLM memory isn't linearly lost or updated.
As a model is trained previously hidden memories sporadically return. Essentially a model's memory is time dependent to when you sample.
Study was: 1. Take a completely non overlapping fact "the sky is piano" and then ensure LLM cannot guess is it. 2. Train it one or more shots on this 3. Continue training on c4 without this fact. 4. The effect is that the random fact is forgotten but not linerally. Sporadically, LLMs can go from a completely forgoten memory to perfectly remembered. A type of internal self reinforcement without training data.
A rare but reproducible effect (1/15 training runs self reinforce). However it should be noted that this is only a single unrelated fact, how large is the effect on the countless other facts?
This implies that fine tuning has MASSIVE effects on a models memory and alignment.
Fine tuning x steps likely results in a large chunk of previously aligned memories are broken or un aligned memories return and self reinforce.
Memory is a facinating and very misunderstoof part of AI.