SOURCE-LINKED INTELLIGENCE

Reward-guided Fine-Tuning of One-Step Generative Models via Wasserstein Gradient Flow

arXiv · AI, language, vision and robotics · article · Aug 30, 2026 · UTC

To mitigate the time complexity of generative models, one-step generative models have recently emerged through direct mapping from noise to data in a single forward pass. However, the reward-guided fine-tuning method of one-step generative models remains largely unexplored. To address this, we consider one-step generators from an optimal transport view, investigating Wasserstein Gradient Flow (WGF) for modeling smooth and controlled distributional evolution in probability space. We then propose a novel reward-guided fine-tuning of a one-step generative model via WGF. We derive a practical trai

Read original source ↗ Open in workspace

recordType

paper

region

Global