Conversely, it is less likely to happen if the optimization process is pursuing a goal that is largely independent from the helper’s output, such as if the helper is an interpretability tool checking for the presence of mesa-optimization.
Maybe I’m missing something obvious here, but for advanced AI systems under monitoring that happen to be misaligned, won’t checking for the presence of mesa-optimization be relevant to the main system’s goal, in cases where the results of those checks matter for whether to re-train or deploy the AI?
However, in practical applications some optimization pressure against the helper will still leak in, for example if the helper is used to decide whether to abort a training run or change its hyperparameters.
Maybe I’m missing something obvious here, but for advanced AI systems under monitoring that happen to be misaligned, won’t checking for the presence of mesa-optimization be relevant to the main system’s goal, in cases where the results of those checks matter for whether to re-train or deploy the AI?
Ah, you make this point here: