Better & Faster Large Language Models via Multi-token Prediction: Algorithmic reasoning

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

4.2. Algorithmic reasoning

Multi-token prediction improves algorithmic reasoning capabilities as measured by this task across task difficulties (Figure 8). In particular, it leads to impressive gains in out-of-distribution generalization, despite the low absolute numbers. Increasing the model size from 30M to 100M parameters, on the other hand, does not improve evaluation accuracy as much as replacing next-token prediction by multi-token prediction does (Figure S16). In Appendix K, we furthermore show that multi-token prediction models retain their advantage over next-token prediction models on this task when trained and evaluated with pause tokens (Goyal et al., 2023).