RLHF and DPO Interview Guide: Alignment, Preference Data, and Production Risks

RLHF and DPO interviews test whether you understand alignment as an engineering pipeline, not a buzzword. Interviewers ask about preference data collection, labeler disagreement, reward model overoptimization, and how alignment objectives interact with tool-use fine-tunes for production agents.

DPO simplified preference optimization but introduced its own pitfalls—implicit reward misspecification and sensitivity to noisy preference pairs. Candidates compare PPO-based RLHF, DPO, ORPO, and KTO-style methods in terms of training stability, compute cost, and ease of iteration for small teams.

Reward hacking appears in production as models that sound helpful while violating policies, or that optimize for verbosity metrics. Discuss how you detect alignment regressions, use held-out human eval, and combine SFT with preference tuning without destroying base model capabilities.

Safety fine-tuning for enterprise products must balance refusal rates, helpfulness, and false refusals on legitimate work tasks. Interviewers want examples of eval slices for jailbreak resistance, PII leakage, and domain-specific compliance—plus rollback strategies when a new alignment pass increases escalations.