arXiv cs.AI· Jinho Chang, Jong Chul Ye·· 11 小时前AI 评分25
ZeNOVA：面向生成模型对齐的流形约束初始噪声优化方法
Manifold-Constrained Initial Noise Optimization for Efficient Generative Model Alignment
AI 导读
ZeNOVA 是一种无梯度初始噪声对齐方法，通过退火软值引导、流形约束超球面 Langevin 动力学和 Metropolis-Hastings 跳跃，解决黑盒奖励场景下现有初始噪声优化依赖一阶梯度导致的不稳定与低效问题。在图像和视频生成模型实验中，ZeNOVA 在更高奖励方向上优于所有评估的零阶基线，且优化过程显著更稳定，并利用了高斯先验的几何结构。
正文 · 原文
Abstract:Recent advances in distillation and flow-map models have enabled deterministic one- or few-step generation for high-quality data, facilitating a new branch of reward alignment approaches that directly optimize the initial noise from a Gaussian distribution. However, most existing initial-noise optimization methods rely on first-order gradient information, which is either inapplicable or suffers from instability and inefficiency in black-box reward scenarios. Here, we introduce ZeNOVA, a stable and efficient initial noise alignment method in a gradient-free manner. Specifically, we address existing algorithms' major challenge in black-box scenarios through annealed soft-value guidance, manifold-constrained hyperspherical Langevin dynamics, and Metropolis-Hastings jumping. Extensive experiments on image and video generative models show that ZeNOVA outperforms all evaluated zeroth-order baselines by optimizing the initial noise toward higher rewards substantially more stably while exploiting the geometry of the Gaussian prior, demonstrating its practical applicability to various black-box reward alignment.
| Comments: | 25 pages, 13 figures |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2610.00365 [cs.LG] |
| (or arXiv:2610.00365v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.00365 arXiv-issued DOI via DataCite |
Submission history
From: Jong Chul Ye [view email]
[v1]
Wed, 30 Sep 2026 06:37:54 UTC (49,194 KB)
[v2]
Thu, 5 Nov 2026 05:23:11 UTC (49,194 KB)
来源：arXiv cs.AI · arxiv.org