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Direct Preference Optimization (DPO)

DPO aligns a model directly from preference pairs (chosen vs rejected) without training a separate reward model or running an RL loop. It derives a closed-form solution to the same KL-constrained objective RLHF optimizes, turning alignment into a simple classification-style loss on the log-ratio between your policy and a frozen reference. FDE loops probe it because it is the practical default for preference tuning, and the trade-offs against PPO-based RLHF are where the judgment lives.

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RELATED CONCEPTS

RLHF (Alignment)Fine-tuning vs RAG vs Prompting

PRACTICE THIS IN REAL QUESTIONS

AI Security, Privacy & GovernanceWhat is data/model poisoning, and how would you detect a backdoor introduced through fine-tuning or embedding data?→Machine Learning & Data ScienceA customer says 'we want the model to be accurate.' How do you turn that into the metric you'll optimize?→LLM & GenAI FundamentalsA customer wants the model to 'know our docs.' Prompting, RAG, or fine-tuning: how do you choose?→RAG & Agent System DesignRetrieval fails on the customer's internal jargon. Fine-tune embeddings, add a reranker, or rewrite queries?→LLM & GenAI FundamentalsWhen does fine-tuning beat few-shot prompting? How much data do you need, and how do you prevent regressions?→Behavioral & Customer ScenariosThe customer insists on fine-tuning when RAG clearly fits. They won't budge. Trusted advisor or vendor, what do you do?→