Equation 7 · What RLHF Actually Optimises: Rated Agreeableness, and Where It Parts from Helpfulness
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Gao, Schulman and Hilton measured this cleanly by building a synthetic setting in which a large fixed “gold” reward model plays the role of the human, and a smaller “proxy” reward model is trained on its labels [ 8 ] . Optimising the proxy raises the gold score at first and then lowers it. They fit empirical functional forms in d , the square root of the KL divergence from the initial policy, finding for best-of- n sampling = d( - d) and for reinforcement learning
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