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arXiv · 2411.11727

Aligning Few-Step Diffusion Models with Dense Reward Difference Learning

Ziyi Zhang·Li Shen·Sen Zhang·Deheng Ye·Yong Luo·Miaojing Shi·Dongjing Shan·Bo Du·Dacheng Tao
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Abstract

Few-step diffusion models enable efficient high-resolution image synthesis but struggle to align with specific downstream objectives due to limitations of existing reinforcement learning (RL) methods in low-step regimes with limited state spaces and suboptimal sample quality. To address this, we propose Stepwise Diffusion Policy Optimization (SDPO), a novel RL framework tailored for few-step diffusion models. SDPO introduces a dual-state trajectory sampling mechanism, tracking both noisy and predicted clean states at each step to provide dense reward feedback and enable low-variance, mixed-step optimization. For further efficiency, we develop a latent similarity-based dense reward prediction strategy to minimize costly dense reward queries. Leveraging these dense rewards, SDPO optimizes a dense reward difference learning objective that enables more frequent and granular policy updates. Additional refinements, including stepwise advantage estimates, temporal importance weighting, and step-shuffled gradient updates, further enhance long-term dependency, low-step priority, and gradient stability. Our experiments demonstrate that SDPO consistently delivers superior reward-aligned results across diverse few-step settings and tasks. Code is available at https://github.com/ZiyiZhang27/sdpo.

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Ziyi Zhang, Li Shen, Sen Zhang, Deheng Ye, Yong Luo, Miaojing Shi, Dongjing Shan, Bo Du, Dacheng Tao. 2026-02-26. Aligning Few-Step Diffusion Models with Dense Reward Difference Learning. https://doi.org/10.1109/tpami.2026.3665753

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