Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Method 2. Method 3. Experiments on real data 3. Experiments on real data 4. Ablations on synthetic data 4. Ablations on synthetic data 5. Why does it work? Some speculation 5. Why does it work? Some speculation 6. Related work 6. Related work 7. Conclusion, Impact statement, Environmental impact, Acknowledgements and References 7. Conclusion, Impact statement, Environmental impact, Acknowledgements and References A. Additional results on self-speculative decoding A. Additional results on self-speculative decoding B. Alternative architectures B. Alternative architectures C. Training speeds C. Training speeds D. Finetuning D. Finetuning E. Additional results on model scaling behavior E. Additional results on model scaling behavior F. Details on CodeContests finetuning F. Details on CodeContests finetuning G. Additional results on natural language benchmarks G. Additional results on natural language benchmarks H. Additional results on abstractive text summarization H. Additional results on abstractive text summarization I. Additional results on mathematical reasoning in natural language I. Additional results on mathematical reasoning in natural language J. Additional results on induction learning J. Additional results on induction learning K. Additional results on algorithmic reasoning K. Additional results on algorithmic reasoning L. Additional intuitions on multi-token prediction L. Additional intuitions on multi-token prediction M. Training hyperparameters M. Training hyperparameters F. Details on CodeContests finetuning We use the Python subset of the CodeContests (Li et al., 2022) train split with reward annotations (“correct” / “incorrect”) and condition on correct solutions at evaluation time. For evaluation, we generate 1000 samples per problem from the test split for each temperature T ∈ {0.5, 0.6, 0.7, 0.8, 0.9}, and compute the unbiased estimator for pass@k from Chen et al. (2021) for each value of k and T. It is possible that models that were pretrained with different losses have different respective optimal temperatures for pass@k, so we compute and show k 7→ maxT pass_at(k, T) in Figure 4. In other words, we grant pass@k access to a temperature oracle. For small values of k, pass@k measures the capability of understanding and solving tasks while for large k, it additionally favors diversity in outputs. According to the results in Figure 4, multi-token prediction pretraining leads to finetuned models that are better on both axes. Authors:
(1) Fabian Gloeckle, FAIR at Meta, CERMICS Ecole des Ponts ParisTech and Equal contribution;
(2) Badr Youbi Idrissi, FAIR at Meta, LISN Université Paris-Saclayand  and Equal contribution;
(3) Baptiste Rozière, FAIR at Meta;
(4) David Lopez-Paz, FAIR at Meta and a last author;
(5) Gabriel Synnaeve, FAIR at Meta and a last author. Authors: Authors: (1) Fabian Gloeckle, FAIR at Meta, CERMICS Ecole des Ponts ParisTech and Equal contribution; (2) Badr Youbi Idrissi, FAIR at Meta, LISN Université Paris-Saclayand  and Equal contribution; (3) Baptiste Rozière, FAIR at Meta; (4) David Lopez-Paz, FAIR at Meta and a last author; (5) Gabriel Synnaeve, FAIR at Meta and a last author. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv

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