What is reward-model overoptimization, and how do you detect and bound it during RLHF?
Push PPO hard enough and true quality peaks then declines 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 Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Push PPO hard enough and true quality peaks then declines 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.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
