Best-of-$N$ Guidance for Test-time Diffusion Alignment
Quick summary
arXiv:2610.05108v2 Announce Type: replace-cross Abstract: 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 the single highest-reward sample. Despite its empirical success, BoN makes limited use of reward information, as it is incorporated only at the final selection stage without influencing the reverse diffusion trajectory durin
Key takeaways
- arXiv:2610.05108v2 Announce Type: replace-cross Abstract: 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 the single highest-reward sample.
Why it matters
This model development creates a new option for users and a new testing obligation for developers. A fixed evaluation set comparing quality, cost and failure behavior is more useful than launch claims.
Member comments