Multi-token prediction improves language model training by allowing models to predict multiple tokens at once instead of one. This method reduces distribution mismatch during training and enhances performance on generative and reasoning tasks. Experiments demonstrate its effectiveness, particularly in larger models with code-related tasks. The integration of speculative decoding with multi-token prediction accelerates inference speeds significantly. Future research will focus on optimizing parameters such as loss scaling and vocabulary size to improve efficiency and model performance in various tasks.
Multi-token prediction presents a novel approach to train language models, improving generative and reasoning tasks by focusing on sequences of tokens rather than individual ones.
Experiments indicate that the proposed method significantly increases efficiency in larger language models, especially benefiting tasks related to code and reducing the mismatch in training and generation.
By implementing speculative decoding alongside multi-token prediction, the inference process can achieve threefold speedup, enhancing overall model performance.
Future research aims to refine multi-token prediction by adjusting loss scales, vocabulary sizes, and auxiliary predictions to promote efficiency and performance of language models.
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