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arXiv · 2510.14751

Beyond Multi-Token Prediction: Pretraining LLMs with Future Summaries

Divyat Mahajan·Sachin Goyal·Badr Youbi Idrissi·Mohammad Pezeshki·Ioannis Mitliagkas·David Lopez-Paz·Kartik Ahuja
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Abstract

Next-token prediction (NTP) has driven the success of large language models (LLMs), but it struggles with long-horizon reasoning, planning, and creative writing, with these limitations largely attributed to teacher-forced training. Multi-token prediction (MTP) partially mitigates these issues by predicting several future tokens at once, but it mostly captures short-range dependencies and offers limited improvement. We propose future summary prediction (FSP), which trains an auxiliary head to predict a compact representation of the long-term future, preserving information relevant for long-form generations. We explore two variants of FSP: handcrafted summaries, for example, a bag of words summary of the future sequence, and learned summaries, which use embeddings produced by a reverse language model trained from right-to-left order. Large-scale pretraining experiments (3B and 8B-parameter models) demonstrate that FSP provides improvements over both NTP and MTP across math, reasoning, and coding benchmarks.

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Divyat Mahajan, Sachin Goyal, Badr Youbi Idrissi, Mohammad Pezeshki, Ioannis Mitliagkas, David Lopez-Paz, Kartik Ahuja. 2025-10-16. Beyond Multi-Token Prediction: Pretraining LLMs with Future Summaries. https://arxiv.org/abs/2510.14751

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