Continual learning might make your blocking monitors nearly useless

Many control protocols work by intervening on an untrusted AI's actions during deployment. For example, you might set up a monitor that scores each action's suspiciousness and blocks actions above a threshold, replacing them with actions from a weaker "trusted" model (a defer-to-trusted protocol). Interventions like this cost usefulness: the protocol sometimes replaces good actions with worse ones.Continual learning, by which I mean any mechanism that lets an AI accumulate skills and knowledge o...