Mechanism
Listwise preference objectives use more than one chosen–rejected pair at a time. Plackett–Luce models assign probabilities to rankings; K-order Ranking Preference Optimization extends this approach to top-K rankings, where reliable feedback may exist only for the leading candidates.
In practice
Keep the feedback structure explicit: a full ordering, a top-K ordering and a set of preferred candidates are different observations. The KPO paper studies ranking tasks and adaptive K; it does not establish that every multi-response DPO method uses the same objective.
References
- K-order Ranking Preference Optimization for Large Language Models ↗
All 8 authors
Shihao Cai, Chongming Gao, Yang Zhang, Wentao Shi, Jizhi Zhang, Keqin Bao, Qifan Wang, Fuli Feng
- Direct Preference Optimization: Your Language Model is Secretly a Reward Model ↗