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cs.LG2026
Noise-Space Reward Alignment for Few-Step Generative Models
Woocheol Shin, Taeyoung Yun, Jiwon Sung +7
Few-step generative models produce high-quality samples in one or a few evaluations. However, steering them toward desired properties remains difficult, especially when only black-…
cs.LG2026
Few-Step Boltzmann Generators via Scalable Likelihood Flow Maps
RuiKang OuYang, Hanlin Yu, Xinyue Ai +7
Recent progress in flow-based generative modeling has led to models that output high-quality samples while using only a small number of function evaluations. However, at present, t…