Core

DPO and Preference-Optimization Variants

Direct Preference Optimization aligns a model directly on preference pairs with a simple classification-style loss, skipping RLHF's separate reward model and RL loop, which makes alignment far simpler and more stable. A family of variants then relaxes DPO's requirements: SimPO removes the reference model, KTO removes the need for paired data, and ORPO merges SFT and alignment into one step. Applied-AI interviews probe it because DPO is now the common way teams align open models, and the variants show you understand what each requirement buys.

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COURSES COVERING THIS TOPIC

No lesson covers this one directly yet. These teach the surrounding topic from the beginning.

Applied AI Engineering·The interviewPremium12mTurning this course into a study planA concrete four-week plan mapping the seven modules onto the question bank, plus what to do differently if your interview is next week rather than next month.

RELATED CONCEPTS

RLHF: Reinforcement Learning from Human Feedback Reward Models LoRA and Parameter-Efficient Fine-Tuning Constitutional AI and RLAIF

PRACTICE THIS IN REAL QUESTIONS

LLM & GenAI FundamentalsWalk through RLHF, then explain DPO and why it has largely displaced PPO-based RLHF.→LLM & GenAI FundamentalsBeyond DPO: what are SimPO, KTO, and ORPO, and why do these alignment variants exist?→LLM & GenAI FundamentalsAfter RLHF, your model is safer but worse at hard tasks. How do you manage the alignment tax?→LLM & GenAI FundamentalsDPO trained cleanly but the model got worse. What are DPO's real failure modes?→LLM & GenAI FundamentalsExplain PPO and GRPO for LLM alignment. Why did GRPO drop the value model?→LLM & GenAI FundamentalsWhat is instruction tuning, and how does it differ from pretraining and alignment?→

COMPANIES THAT ASSUME THIS

OpenAI Anthropic Google Microsoft Scale AI Decagon

NEXT IN FOUNDATIONS OF LLMS & GENAIPolicy Optimization: PPO and GRPO→