Patch-level Training For Large Language Models

Shao Chenze, Meng Fandong, Zhou Jie. Arxiv 2024

[Paper] [Code]    
Efficiency And Optimization Has Code Reinforcement Learning Training Techniques

As Large Language Models (LLMs) achieve remarkable progress in language understanding and generation, their training efficiency has become a critical concern. Traditionally, LLMs are trained to predict the next token in a sequence. Despite the success of token-level training, it suffers from considerable computational costs due to the need to process an extensive number of tokens. To mitigate this issue, this paper introduces patch-level training for LLMs, which reduces the sequence length by compressing multiple tokens into a single patch. During patch-level training, we feed the language model shorter sequences of patches and train it to predict the next patch, thereby processing the majority of the training data at a significantly reduced computational cost. Following this, the model continues token-level training on the remaining training data to align with the inference mode. Experiments on a diverse range of models (370M-2.7B parameters) demonstrate that patch-level training can reduce overall computational costs to 0.5\(\times\), without compromising the model performance compared to token-level training. Source code: \url{https://github.com/shaochenze/PatchTrain}.

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