Towards General Preference Alignment: Diffusion Models at Nash Equilibrium

• URL: http://arxiv.org/abs/2605.04494v1
• Date: Wed, 06 May 2026 04:50:42 GMT
• Title: Towards General Preference Alignment: Diffusion Models at Nash Equilibrium
• Abstract summary: We propose an intuitive general preference framework for diffusion alignment.<n>Diff.-NPO encourages the current policy to play against itself to achieve self improvement and lead to a better alignment.<n>We demonstrate the effectiveness of Diff.-NPO on the text-to-image generation task via various metrics.
• Score: 12.039242093129625
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement learning from human feedback (RLHF) has been popular for aligning text-to-image (T2I) diffusion models with human preferences. As a mainstream branch of RLHF, Direct Preference Optimization (DPO) offers a computationally efficient alternative that avoids explicit reward modeling and has been widely adopted in diffusion alignment. However, existing preference-based methods for diffusion alignment still rely on reward-induced preference signals and typically assume that human preferences can be adequately modeled by the Bradley--Terry (BT) model, which may fail to capture the full complexity of human preferences. In this paper, we formulate diffusion alignment from a game-theoretic perspective. We propose Diffusion Nash Preference Optimization (Diff.-NPO), an intuitive general preference framework for diffusion alignment. Diff.-NPO encourages the current policy to play against itself to achieve self improvement and lead to a better alignment. Empirically, we demonstrate the effectiveness of Diff.-NPO on the text-to-image generation task via various metrics. Diff.-NPO consistently outperforms existing preference-based diffusion alignment methods.

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