FlowCPO: A Unified Divergence View of Preference Alignment for Flow Models
Preference alignment for flow and diffusion models now spans online reinforcement learning and offline preference optimization, but the relation between these methods remains unclear. In particular, existing forward-process alignment methods require fresh samples from the current model, while offline methods based on fixed preference pairs rely primarily on positive-only fine-tuning or DPO-style likelihood-ratio surrogates. We organize these approaches through a divergence-based framework and introduce FlowCPO, an offline forward-KL objective that uses both preferred and dispreferred samples without online rollouts. For linear interpolation, we show under explicit regularity conditions th...
| Authors | Yansen Han, Shengyi Liao, Peng Sun et al. |
|---|---|
| Primary category | stat.ML |
| Published | 9 September 2026 |
| Last updated at source | 9 September 2026 |
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