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Research paper details how multi-token prediction aids Transformer planning

By PulseAugur Editorial · [1 sources] ·

A new research paper explores how multi-token prediction (MTP) enables Transformers to perform planning and reasoning tasks more effectively than standard next-token prediction (NTP). The study demonstrates that MTP outperforms NTP on graph path-finding and reasoning benchmarks like Countdown. Theoretically, MTP encourages a backward reconstruction of paths by first attending to the end node, a process facilitated by a gradient decoupling property that offers a cleaner training signal. AI

IMPACT This research could lead to more capable AI systems for planning and complex reasoning tasks.

RANK_REASON The cluster contains an academic paper detailing a novel training objective for language models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Research paper details how multi-token prediction aids Transformer planning

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The cluster contains an academic paper detailing a novel training objective for language models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

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

    How Transformers Learn to Plan via Multi-Token Prediction

    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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