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Core
DPO and Preference-Optimization Variants
Direct Preference Optimization aligns a model straight from preference pairs with a simple classification-style loss, bypassing RLHF's separate reward model and RL loop, which makes alignment far simpler and more stable. A family of variants then loosens DPO's requirements: SimPO drops the reference model, KTO drops the need for paired data, and ORPO folds SFT and alignment into one step. AI, ML, and GenAI 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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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?→
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