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English(EN) How Transformers Learn to Plan via Multi-Token Prediction

研究论文详细介绍了多令牌预测如何帮助 Transformer 进行规划

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新研究论文探讨了多令牌预测（MTP）如何使 Transformer 比标准的下一令牌预测（NTP）更有效地执行规划和推理任务。研究表明，在图路径查找和 Countdown 等推理基准测试中，MTP 的表现优于 NTP。理论上，MTP 通过首先关注结束节点来鼓励路径的向后重建，这一过程得益于梯度解耦特性，该特性提供了更清晰的训练信号。 AI

影响 这项研究可能带来更强大的 AI 系统，用于规划和复杂的推理任务。

排序理由 该集群包含一篇详细介绍语言模型新训练目标的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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研究论文详细介绍了多令牌预测如何帮助 Transformer 进行规划

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该集群包含一篇详细介绍语言模型新训练目标的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jianhao Huang, Zhanpeng Zhou, Renqiu Xia, Baharan Mirzasoleiman, Weijie Su, Wei Huang ·

    Transformer 如何通过多令牌预测学习规划

    arXiv:2604.11912v2 Announce Type: replace-cross Abstract: While next-token prediction (NTP) has been the standard objective for training language models, it often struggles to capture global structure in reasoning tasks. Multi-token prediction (MTP) has recently emerged as a prom…

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