Reward-guided Fine-Tuning of One-Step Generative Models via Wasserstein Gradient Flow
This work considers one-step generators from an optimal transport view, investigating Wasserstein Gradient Flow (WGF) for modeling smooth and controlled distributional evolution in probability space, and proposes a novel reward-guided fine-tuning of a one-step generative model via WGF.
Hoseong Hwang, Woorim Han, Joungin Chun et al.
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