In reinforcement studying from human suggestions, it is not uncommon to optimize in opposition to a reward mannequin skilled to foretell human preferences. As a result of the reward mannequin is an imperfect proxy, optimizing its worth an excessive amount of can hinder floor reality efficiency, in accordance with Goodhart’s regulation. This impact has been continuously noticed, however not rigorously measured because of the expense of amassing human choice information. On this work, we use an artificial setup through which a set « gold-standard » reward mannequin performs the function of people, offering labels used to coach a proxy reward mannequin. We research how the gold reward mannequin rating modifications as we optimize in opposition to the proxy reward mannequin utilizing both reinforcement studying or best-of-n sampling. We discover that this relationship follows a special practical kind relying on the tactic of optimization, and that in each circumstances its coefficients scale easily with the variety of reward mannequin parameters. We additionally research the impact on this relationship of the scale of the reward mannequin dataset, the variety of reward mannequin and coverage parameters, and the coefficient of the KL penalty added to the reward within the reinforcement studying setup. We discover the implications of those empirical outcomes for theoretical issues in AI alignment.