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FlowCPO introduces unified divergence view for preference alignment in flow models

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced FlowCPO, a novel method for aligning flow and diffusion models using an offline forward-KL objective. This approach unifies existing online reinforcement learning and offline preference optimization techniques by utilizing both preferred and dispreferred samples without requiring fresh model rollouts. FlowCPO offers a tractable surrogate loss based on contrastive flow matching, which is bounded and non-negative, unlike some existing methods that can be unbounded below. In evaluations, FlowCPO demonstrated improved performance on in-domain GenEval and OCR tasks compared to baselines like FlowDPO. AI

IMPACT Introduces a new method for aligning generative models, potentially improving their performance on specific tasks.

RANK_REASON The cluster contains an academic paper detailing a new method for flow and diffusion models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

FlowCPO introduces unified divergence view for preference alignment in flow models

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The cluster contains an academic paper detailing a new method for flow and diffusion models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Yansen Han, Shengyi Liao, Peng Sun, Deyuan Liu, Yuanxing Zhang, Pengfei Wan, Tao Lin ·

    FlowCPO: A Unified Divergence View of Preference Alignment for Flow Models

    arXiv:2609.09905v1 Announce Type: new Abstract: 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 alignmen…

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