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cs.LG2026
Best-of- Guidance for Test-time Diffusion Alignment
Richard Lee Kim, Yeongmin Kim, Gyuwon Sim +3
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 algor…
cs.LG2026
Lookahead Sample Reward Guidance for Test-Time Scaling of Diffusion Models
Yeongmin Kim, Donghyeok Shin, Byeonghu Na +3
Diffusion models have demonstrated strong generative performance; however, generated samples often fail to fully align with human intent. This paper studies an efficient test-time…