Table of Links
Abstract and 1. Introduction
2. Method
3. Experiments on real data
3.1. Benefits scale with model size and 3.2. Faster inference
3.3. Learning global patterns with multi-byte prediction and 3.4. Searching for the optimal n
3.5. Training for multiple epochs and 3.6. Finetuning multi-token predictors
3.7. Multi-token prediction on natural language
4. Ablations on synthetic data and 4.1. Induction capability
4.2. Algorithmic reasoning
5. Why does it work? Some speculation and 5.1. Lookahead reinforces choice points
5.2. Information-theoretic argument
6. Related work
7. Conclusion, Impact statement, Environmental impact, Acknowledgements, and References
A. Additional results on self-speculative decoding
B. Alternative architectures
C. Training speeds
D. Finetuning
E. Additional results on model scaling behavior
F. Details on CodeContests finetuning
G. Additional results on natural language benchmarks
H. Additional results on abstractive text summarization
I. Additional results on mathematical reasoning in natural language
J. Additional results on induction learning
K. Additional results on algorithmic reasoning
L. Additional intuitions on multi-token prediction
M. Training hyperparameters
2. Method
Standard language modeling learns about a large text corpus x1, . . . xT by implementing a next-token prediction task. Formally, the learning objective is to minimize the cross-entropy loss
In this work, we generalize the above by implementing a multi-token prediction task, where at each position of the training corpus, the model is instructed to predict n future tokens at once. This translates into the cross-entropy loss
Authors:
(1) Fabian Gloeckle, FAIR at Meta, CERMICS Ecole des Ponts ParisTech, and contributed equally;
(2) Badr Youbi IdrissiFAIR at Meta, LISN Université Paris-Saclay, and contributed equally;
(3) Baptiste Rozière, FAIR at Meta;
(4) David Lopez-Paz, FAIR at Meta and his the last author;
(5) Gabriel Synnaeve, FAIR at Meta and his the last author.
