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About this interview
A technical interview on Preference Alignment & RLHF, pitched at the hard level. A voice AI interviewer leads the conversation, adapts its questions to your answers, keeps you on topic, and afterward gives you honest, specific feedback on where you were strong and where to improve. Expect roughly 30 minutes.
What you'll be assessed on
Describe the RLHF pipeline: reward model training, PPO policy update, and the KL-divergence constraint
Explain Direct Preference Optimization (DPO): how it eliminates the explicit reward model and its trade-offs
Explain GRPO and how group relative rewards differ from a trained reward model
Explain rejection sampling for preference data and why on-policy data improves alignment stability
Topics covered
MotivationRLHF PipelineReward ModelKL ConstraintPPO in RLHFDPODPO vs PPORejection SamplingOn-Policy vs Off-PolicyGRPOFailure ModesRLAIFReward ModelsAlignment Algorithms
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“In plain terms, what problem does preference alignment solve that supervised fine-tuning alone cannot?
“DPO still has a reference policy and a beta parameter. What does beta control, and what happens at very small versus very large beta values?
“How does the Generalized Advantage Estimation algorithm work in PPO, and why does it reduce variance in policy gradient updates?