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arXiv · 2503.21801

Efficient Joint Prediction of Multiple Future Tokens

Kwangjun Ahn·Alex Lamb·John Langford
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Abstract

In this short report, we introduce joint multi-token prediction (JTP), a lightweight modification of standard next-token prediction designed to enrich hidden state representations by jointly predicting multiple future tokens. Unlike previous multi-token prediction approaches, JTP strategically employs teacher forcing of future-tokens through a carefully designed representation bottleneck, allowing the model to encode rich predictive information with minimal computational overhead during training. We show that the JTP approach achieves a short-horizon belief state representation, while popular alternatives for multi-token prediction fail to do so. We demonstrate the effectiveness of our method on the synthetic star graph navigation task from from Bachmann and Nagarajan [2024], highlighting a significant performance improvement over existing methods. This manuscript presents promising preliminary results intended to stimulate further research.

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Kwangjun Ahn, Alex Lamb, John Langford. 2025-03-24. Efficient Joint Prediction of Multiple Future Tokens. https://arxiv.org/abs/2503.21801

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