Linear-DPO: Linear Direct Preference Optimization for Diffusion and Flow-Matching Generative Models

• URL: http://arxiv.org/abs/2605.21123v1
• Date: Wed, 20 May 2026 12:54:51 GMT
• Title: Linear-DPO: Linear Direct Preference Optimization for Diffusion and Flow-Matching Generative Models
• Abstract summary: We derive a generalized DPO objective that covers both diffusion and flow-matching via a unified reverse-time SDE framework.<n>We propose Linear-DPO, which replaces the aggressive sigmoid-based utility function with a sustained linear utility and incorporates an EMA-updated reference model.
• Score: 5.527466367347639
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Direct Preference Optimization (DPO) is successful for alignment in LLMs but still faces challenges in text-to-image generation. Existing studies are confined to denoising diffusion models while overlooking flow-matching, and suffer from an objective mismatch when applying discrete NLP-based DPO to regression-based generative tasks.\ In this paper, we derive a generalized DPO objective that covers both diffusion and flow-matching via a unified reverse-time SDE framework, and point out from a gradient perspective that the standard DPO objective is suboptimal for text-to-image generation. Consequently, we propose Linear-DPO, which replaces the aggressive sigmoid-based utility function with a sustained linear utility and incorporates an EMA-updated reference model. Qualitative and quantitative experiments on diffusion models (SD1.5, SDXL) and flow-matching model (SD3-Medium) demonstrate the superiority of our approach over existing baselines.

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