Continual learning might make your blocking monitors nearly useless

Abstract

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 over the course of its deployment, optimizes for usefulness. So, if blocking monitors get in the way of task success, I expect continually-learning AIs to learn how to evade blocking [1] monitors. This requires no scheming; it falls out of usefulness pressure applied to a benign model. And the effect is plausibly substantial: a long deployment with online RL (for example) amounts to training the policy against the monitor, and could involve enough optimization to leave blocking monitors nearly useless. While the problem would be noticeable to developers, I argue it is hard to fix. Because monitor evasion looks like legitimate learning to your continual learning system, it's hard to have one

Research area