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English(EN) LatticeSMC: Where to Spend Inference-Time Compute in Chunked Sequence Generators

LatticeSMC采样器优化分块序列生成器的推理计算

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了LatticeSMC，一种新颖的采样方法，旨在优化分块生成序列（如音乐、动作和视频）的推理时间计算。该方法利用了表示块索引和去噪步骤的二维格点上的Feynman-Kac模型，允许基于奖励结构进行精确的设计选择。在文本到音乐生成任务中，LatticeSMC在节拍对齐和提示遵循等指标上表现出显著的改进，在匹配的计算预算下优于现有的如best-of-N采样等方法。 AI

影响 优化生成模型的推理计算，可能在多媒体应用中带来更高效、更高质量的输出。

排序理由 该集群包含一篇详细介绍序列生成新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

LatticeSMC采样器优化分块序列生成器的推理计算

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该集群包含一篇详细介绍序列生成新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Xuanchen Wang, Heng Wang, Weidong Cai ·

    LatticeSMC：在分块序列生成器中花费推理时间计算的去向

    arXiv:2610.02774v1 Announce Type: new Abstract: Long-form generators for music, motion and video produce sequences chunk by chunk, with each chunk generated by iterative denoising while rewards are defined over the full sequence. Existing inference-time steering methods typically…

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