Alignment

Align LLMs with Direct Preference Optimization

Align LLMs with Direct Preference Optimization Supervised fine-tuning works well when you can provide a target response for each prompt. It becomes less natural when the signal is comparative: one answer is preferred over another, but neither is a perfect target to copy. Direct Preference Optimization (DPO) turns those preference pairs into a training objective for a language model without requiring a separately trained reward model or an online reinforcement step.