Beyond Pairs: Your Language Model is Secretly Optimizing a Preference Graph
Direct Preference Optimization (DPO) aligns language models using pairwise preference comparisons, offering a simple and effective alternative to Reinforcement Learning (RL) from human feedback. However, in many practical settings, training data consists of multiple rollouts per prompt, inducing rich preference structure that pairwise DPO fails to exploit. Collapsing such data into independent pairs discards transitivity, introduces redundant or conflicting supervision, and can lead to unstable optimization. We propose Graph Direct Preference Optimization (GraphDPO), a structured generalization of DPO that operates over directed acyclic preference graphs induced by rollout rankings. Graph...
| Authors | Ning Liu, Chuanneng Sun, Kristina Klinkner et al. |
|---|---|
| Primary category | cs.LG |
| Published | 8 May 2026 |
| Last updated at source | 8 October 2026 |
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