Step-level Denoising-time Diffusion Alignment with Multiple Objectives

• URL: http://arxiv.org/abs/2604.14379v1
• Date: Wed, 15 Apr 2026 19:52:05 GMT
• Title: Step-level Denoising-time Diffusion Alignment with Multiple Objectives
• Abstract summary: Reinforcement learning (RL) has emerged as a powerful tool for aligning diffusion models with human preferences.<n>In practice, however, human preferences are inherently pluralistic, and aligned models must balance multiple downstream objectives.<n>We propose Multi-objective Step-level Denoising-time Diffusion Alignment (MSDDA), a retraining-free framework for aligning diffusion models with multiple objectives.
• Score: 26.715620800855962
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement learning (RL) has emerged as a powerful tool for aligning diffusion models with human preferences, typically by optimizing a single reward function under a KL regularization constraint. In practice, however, human preferences are inherently pluralistic, and aligned models must balance multiple downstream objectives, such as aesthetic quality and text-image consistency. Existing multi-objective approaches either rely on costly multi-objective RL fine-tuning or on fusing separately aligned models at denoising time, but they generally require access to reward values (or their gradients) and/or introduce approximation error in the resulting denoising objectives. In this paper, we revisit the problem of RL fine-tuning for diffusion models and address the intractability of identifying the optimal policy by introducing a step-level RL formulation. Building on this, we further propose Multi-objective Step-level Denoising-time Diffusion Alignment (MSDDA), a retraining-free framework for aligning diffusion models with multiple objectives, obtaining the optimal reverse denoising distribution in closed form, with mean and variance expressed directly in terms of single-objective base models. We prove that this denoising-time objective is exactly equivalent to the step-level RL fine-tuning, introducing no approximation error. Moreover, we provide numerical results, which indicate our method outperforms existing denoising-time approaches.

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