Research paper details how multi-token prediction aids Transformer planning

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 →

• arXiv
• Countdown
• Jianhao Huang
• Multi Token Prediction
• next-token prediction
• transformers

• paper
• model release

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