The Direct Preference Optimization Family

Rafailov et al. (2023) showed RLHF's optimum has a closed form in terms of the preference data, so you can skip the explicit reward model and optimize the policy directly. That insight spawned a family — IPO, KTO, SimPO, ORPO, BPO — each fixing a failure mode of DPO. In 2026, direct alignment algorithms ship more frontier post-training runs than PPO. But the over-optimization curve from Lesson 2 still applies: DAAs do not escape Goodhart, they just move where it bites.