Better & Faster Large Language Models via Multi-token Prediction (AI summary)

Key Points

1. The paper proposes a multi-token prediction strategy to train large language models, suggesting that training language models to predict multiple future tokens at once leads to higher sample efficiency.

2. The multi-token prediction strategy involves training the model to predict the following n tokens at each position in the training corpora using n independent output heads, resulting in improved downstream capabilities with no additional training time.

3. Experimental evidence supports the effectiveness of multi-token prediction at a larger scale, with models up to 13B parameters solving around 15% more code problems on average.

4. Multi-token prediction leads to faster inference, with models trained with 4-token prediction being up to 3 times faster at inference, even with large batch sizes.

5. The paper demonstrates that multi-token prediction is increasingly useful with growing model size, significantly improving performance on tasks, especially in generative benchmarks like coding.

6. Memory-efficient implementation is proposed to reduce GPU memory utilization when training multi-token predictors, as naive implementations can limit batch size and GPU memory utilization. The proposed architecture reduces peak GPU memory utilization at no runtime expense.

7. The research also highlights the potential of multi-token prediction for learning global patterns, promoting longer-term patterns and algorithmic reasoning capabilities.

8. The study explores the implications of multi-token prediction on various tasks, including coding, natural language tasks, and byte-level training, indicating consistent performance improvements across different types of models and benchmarks.

9. The paper presents future research directions, such as automating the choice of n in multi-token prediction, exploring optimal vocabulary sizes, and developing improved auxiliary prediction losses operating in embedding spaces. Additionally, considerations for environmental impact and the need for further exploration of multi-target prediction in various domains are discussed.

Summary

The research paper "Better & Faster Large Language Models via Multi-token Prediction" explores the benefits of training language models to predict multiple future tokens at once using independent output heads and a shared model trunk. The paper argues that this approach improves sample efficiency and downstream capabilities for both code and natural language models, especially for larger model sizes and across multiple epochs of training.

The authors propose training language models to predict multiple future tokens at once using n independent output heads operating on top of a shared model trunk, which leads to higher sample efficiency and improved downstream capabilities with no increase in training time for both code and natural language models. The method is particularly useful for larger model sizes and across multiple training epochs. The findings suggest that this approach results in significant gains on generative benchmarks such as coding tasks, with their 13B parameter models solving 12% more problems on HumanEval and 17% more on MBPP than comparable next-token models. Moreover, the approach also demonstrates improvements in problem-solving capabilities, as models trained with multi-token prediction are up to 3 times faster at inference, even with large batch sizes.

The researchers argue that multi-token prediction leads to qualitative changes in model capabilities and generalization behaviors. It encourages planning, improves representations, and prevents overfitting on local patterns that can result from teacher-forced training. The paper presents experimental evidence that multi-token prediction improves learning longer-term patterns and showcases the benefits of multi-token prediction across a wide range of scenarios, including code tasks, algorithmic reasoning capabilities, and generative evaluations such as summarization.

The authors also discuss the memory-efficient implementation of multi-token predictors to reduce GPU memory utilization. They demonstrate how this approach is increasingly useful as the model size grows, leading to better performance and unlocking efficient byte-level training.

In conclusion, the paper highlights the effectiveness of multi-token prediction in training stronger and faster transformer models, presenting evidence of improved downstream performance on various tasks and the potential for significant speedups in inference time. The authors emphasize the need for further exploration to understand how to automatically choose the number of predicted tokens and to develop improved auxiliary prediction losses that operate in embedding spaces.

Overall, the research paper provides a comprehensive and detailed exploration of the benefits of multi-token prediction in training large language models, presenting experimental evidence and insights into the potential improvements in sample efficiency, downstream capabilities, and inference speed.

Reference: https://arxiv.org/abs/2404.197...