Your question is Reward Hacking in RLHF. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
How do you handle reward hacking during Reinforcement Learning from Human Feedback (RLHF)?
Explain how you would detect when a policy exploits weaknesses in a reward model rather than improving the intended behavior. Discuss reward-model validation, held-out evaluations, adversarial testing, independent human judgments, and training controls such as KL regularization or conservative policy updates.
Provide a practical mitigation plan, pseudocode or Python for one detection or evaluation component, and concrete metrics you would monitor. Address trade-offs between reward optimization, capability, alignment, evaluation cost, and distribution shift.
