Patch-Level Training for Large Language Models
<|reference_start|>Patch-Level Training for Large Language Models: 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}.<|reference_end|>
@article{shao2024patch-level,
    title={Patch-Level Training for Large Language Models},
    author={Chenze Shao, Fandong Meng, Jie Zhou},
    journal={arXiv preprint arXiv:2407.12665},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.12665},
    primaryClass={cs.CL cs.AI cs.LG}
}