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hardOpenAIAnthropicGoogle DeepMind

What is reward-model overoptimization, and how do you detect and bound it during RLHF?

Push PPO hard enough and true quality peaks then falls while the reward keeps climbing. The signal is knowing the Gold-vs-proxy gap, the KL budget that bounds it, and how you actually measure when to stop.

Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.

Push PPO hard enough and true quality peaks then falls while the reward keeps climbing. The signal is knowing the Gold-vs-proxy gap, the KL budget that bounds it, and how you actually measure when to stop.

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