  Large language models such as GPT and Llama are trained with a next-token
prediction loss. In this work, we suggest that training language models to
predict multiple future tokens at once results in higher sample efficiency.
More specifically, at each position in the training corpus, we ask the model to
predict the following n tokens using n independent output heads, operating on
top of a shared model trunk. Considering multi-token prediction as an auxiliary
training task, we measure improved downstream capabilities with no overhead in
training time for both code and natural language models. The method is
increasingly useful for larger model sizes, and keeps its appeal when training
for multiple epochs. Gains are especially pronounced on generative benchmarks
like coding, where our models consistently outperform strong baselines by
several percentage points. Our 13B parameter models solves 12 % more problems
on HumanEval and 17 % more on MBPP than comparable next-token models.
Experiments on small algorithmic tasks demonstrate that multi-token prediction
is favorable for the development of induction heads and algorithmic reasoning
capabilities. As an additional benefit, models trained with 4-token prediction
are up to 3 times faster at inference, even with large batch sizes.
