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Author

Taekyu Kim

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Preprint Oct 2026

Best-of-$N$ Guidance for Test-time Diffusion Alignment

Diffusion models achieve strong generative performance but often struggle to align generated samples with human preferences measured by a reward model. A simple yet effective algorithm for test-time alignment is Best-of-$N$ (BoN) sampling, which draws $N$ i.i.d. samples from a pre-trained diffusion model and outputs th...

Richard Kim, Yeongmin Kim, Gyuwon Sim et al. · 0 citations
#artificial intelligence Preprint Oct 2026

Best-of-$N$ Guidance for Test-time Diffusion Alignment

Diffusion models achieve strong generative performance but often struggle to align generated samples with human preferences measured by a reward model. A simple yet effective algorithm for test-time alignment is Best-of-$N$ (BoN) sampling, which draws $N$ i.i.d. samples from a pre-trained diffusion model and outputs th...

Richard Kim, Yeongmin Kim, Gyuwon Sim et al. · 0 citations

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