[
    {
        "id": "osp-20223",
        "type": "article-journal",
        "title": "Manifold-Constrained Initial Noise Optimization for Efficient Generative Model Alignment",
        "author": [
            {
                "family": "Chang",
                "given": "Jinho"
            },
            {
                "family": "Ye",
                "given": "Jong Chul"
            }
        ],
        "URL": "https://omanscience.com/ar/articles/manifold-constrained-initial-noise-optimization-for-efficient-generative-model-alignment",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "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."
    }
]