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English(EN) TIGER: Text-Conditioned Visual Gated Routing with Acceptance Alignment for Multimodal Speculative Decoding

TIGER框架加速多模态推测解码以用于VLMs

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了TIGER，一种新颖的多模态推测解码框架，旨在加速视觉语言模型（VLMs）的生成过程。与以前的方法不同，TIGER根据文本的当前状态动态选择相关的视觉令牌，而不是使用固定的压缩接口。这种方法使用接受对齐策略训练来优化草稿模型，鼓励它生成更有可能被更大的目标模型接受的续写。实验表明，TIGER在保持下游准确率相当的同时，提高了接受的前缀长度和推测加速比。 AI

影响 这项研究可能带来更快、更高效的多模态AI模型，改善用户体验并支持新应用。

排序理由 该集群包含一篇详细介绍多模态推测解码新方法的论文。

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

TIGER框架加速多模态推测解码以用于VLMs

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报道来源 [2]

  1. arXiv cs.CL TIER_1 English(EN) · Quynh Vo, Cong-Duy Nguyen, Ponhvoan Srey, Luu Anh Tuan, Thong Nguyen ·

    TIGER：用于多模态推测解码的文本条件视觉门控路由与接受对齐

    arXiv:2607.11131v1 Announce Type: new Abstract: Speculative decoding accelerates autoregressive generation by letting a lightweight drafter propose multiple tokens that are verified by a larger target model. Although effective for text-only LLMs, speculative decoding yields limit…

  2. arXiv cs.CL TIER_1 English(EN) · Thong Nguyen ·

    TIGER：用于多模态推测解码的文本条件视觉门控路由与接受对齐

    Speculative decoding accelerates autoregressive generation by letting a lightweight drafter propose multiple tokens that are verified by a larger target model. Although effective for text-only LLMs, speculative decoding yields limited gains in VLMs because drafters often diverge …

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