English(EN) Trajectory-Triggered Sparse Collaborative Decoding

新的解码方法通过轨迹预测提高语言模型效率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-19 04:00

研究人员开发了一种名为轨迹触发的稀疏协作解码的新方法，用于提高语言模型的效率。与持续验证草稿令牌的推测解码不同，该方法仅在简短的纠正片段时调用目标模型。关键创新在于使用草稿模型置信度路径的聚合轨迹形状特征来预测何时触发目标模型，达到了 0.86 的 AUC。该方法在 GSM8K、MATH-500 和 StrategyQA 等各种基准测试中显示出显著的准确性提高，优于开环调度。 AI

影响通过减少目标模型调用来提高语言模型推理的效率。

排序理由详细介绍语言模型解码新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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报道来源 [1]

arXiv cs.CL TIER_1 English(EN) · Amit Ranjan Trivedi, Sruthikesh Surineni, Kyong Ho Lee, Mohit Mittal, Steven Li, Rex Hill · 2026-10-19 04:00

轨迹触发的稀疏协作解码

arXiv:2610.23613v1 Announce Type: new Abstract: Dense assistance methods such as speculative decoding repeatedly verify draft tokens with a target model, keeping target computation coupled to generation. We study sparse collaborative decoding, where a draft model generates most t…

本文如何被排名

报道来源 [1]

轨迹触发的稀疏协作解码

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