English(EN) Learning Noise Optimization for Reward Alignment in Few-Step Generative Models

新的LeNO方法优化生成模型的奖励对齐

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-05 05:00

研究人员开发了LeNO（学习噪声优化），一种用于改进少样本生成模型奖励对齐的新颖方法。该方法将奖励引导的噪声优化分摊到学习到的残差传输中，消除了推理过程中昂贵的每样本搜索的需要。LeNO使用局部GRPO和标量奖励学习噪声校正的锚点条件分布，该方法已在文本到图像和蛋白质结构生成任务上进行了评估。该方法通过一次噪声校正来增强目标奖励，同时保持生成质量和多样性。 AI

影响这项研究可能导致更高效、更有效的少样本生成模型，特别是在需要特定奖励引导输出的应用中。

排序理由这是一篇详细介绍生成模型新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Hongeun Kim, Jeongsol Kim, Jong Chul Ye · 2026-11-05 05:00

少样本生成模型中用于奖励对齐的学习噪声优化

arXiv:2611.06255v1 Announce Type: new Abstract: Reward alignment for diffusion- and flow-based generative models is often formulated over stochastic trajectories, making it less suitable for few-step deterministic generators. Initial noise optimization provides a trajectory-free …

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少样本生成模型中用于奖励对齐的学习噪声优化

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