arXiv cs.AI· Richard Lee Kim, Yeongmin Kim, Gyuwon Sim, Taekyu Kim, Minsang Park, Il-chul Moon·· 4 小时前AI 评分47
BoNG：面向测试时扩散对齐的 Best-of-N 引导方法
Best-of-$N$ Guidance for Test-time Diffusion Alignment
AI 导读
论文提出 Best-of-N Guidance（BoNG），将 Best-of-N 采样原理融入扩散模型的反向扩散过程，用当前 BoN 粒子引导其余去噪粒子群向高奖励区域移动，从而同时提升最佳样本与平均样本质量。
正文 · 原文
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 during sampling. Consequently, BoN sampling does not improve the average alignment of generated samples and is primarily suited to single-output settings. We propose Best-of-$N$ Guidance (BoNG), a novel method that integrates the principle of BoN sampling directly into the reverse diffusion process. BoNG performs online BoN selection over denoising particles and adjusts the reverse diffusion process to steer the particle population toward higher-reward regions during generation. Specifically, by introducing an asymmetric guidance interaction among denoising particles, BoNG uses the current BoN particle as a guidance signal to the rest of the particle population. This particle-level interaction reshapes the sampling process toward higher-reward regions, enabling BoNG to improve not only the final best sample beyond Vanilla BoN sampling, but also the average quality of generated samples. Over 36 empirical comparisons, BoNG achieves the best performance in 29 cases, ranking first in 80.56% of the comparisons against SMC and Vanilla BoN sampling. BoNG also supports multi-output capability, achieving 1.3$\times$ ImageReward score of the latest sample-based guidance method with a 1.6$\times$ speedup. We release the code at this https URL.
| Comments: | NeurIPS 2026 |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.05108 [cs.LG] |
| (or arXiv:2610.05108v3 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.05108 arXiv-issued DOI via DataCite |
Submission history
From: Richard Lee Kim [view email]
[v1]
Sun, 4 Oct 2026 10:35:32 UTC (40,380 KB)
[v2]
Tue, 6 Oct 2026 07:19:42 UTC (2,042 KB)
[v3]
Thu, 5 Nov 2026 07:53:14 UTC (2,050 KB)
来源：arXiv cs.AI · arxiv.org