Yeongmin Kim

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

WASD: Wasserstein-based Knowledge Distillation for Large Language Models

Autoregressive large language models (LLMs) have rapidly advanced in capability, but their increasing scale comes with substantial computational and memory costs at inference time. Knowledge distillation (KD) offers a practical solution by transferring knowledge from a large teacher model to a smaller student model via...

Byeonghu Na, Donghyeok Shin, Yeongmin Kim 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