DIDR：面向单步生成器强化学习的扩散奖励框架

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Abstract:Recent advances in one-step text-to-image generation have enabled real-time synthesis with remarkable efficiency and quality. Previous reinforcement learning methods for one-step generators combine image-space reward optimization with diffusion noisy-space distribution matching. This paradigm brings challenges due to a mismatch between terminal reward optimization and the underlying generative dynamics. As a result, optimization tends to exploit stochastic degrees of freedom, often improving reward at the expense of image fidelity. To address this issue, we propose Diff-Instruct with Diffused Reward (DIDR), a data-free trajectory-level alignment framework derived from Integral KL minimization. DIDR propagates the RLHF-optimal reward-tilted clean-image distribution across all noise levels along the diffusion trajectory. We show that this objective admits the same minimizer as clean-image RLHF, while naturally inducing the Diffused Reward Score (DRS), which acts as a reward-driven correction to the reference score function. To make this practical, we further introduce the Diffused Reward Proxy (DRP), an efficient estimator of DRS based on differentiable short-step denoising. Extensive experiments demonstrate that DIDR consistently Pareto-dominates existing one-step SDXL baselines. Moreover, when transferred to a 6B DiT backbone (Z-Image), DIDR surpasses its 50-step teacher in preference alignment while requiring only a single generation step. Code: this https URL

Comments:	33 pages. Updated manuscript and author information
Subjects:	Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as:	arXiv:2605.24001 [cs.CV]
	(or arXiv:2605.24001v3 [cs.CV] for this version)
	https://doi.org/10.48550/arXiv.2605.24001 arXiv-issued DOI via DataCite

Submission history

From: Junyi Wu [view email]
[v1] Mon, 18 May 2026 21:26:33 UTC (22,870 KB)
[v2] Tue, 26 May 2026 04:35:31 UTC (22,871 KB)
[v3] Tue, 10 Nov 2026 18:16:41 UTC (22,870 KB)