STEP: Staged Parameter-Efficient Pre-training for Large Language Models

Kazuki Yano; Takumi Ito; Jun Suzuki

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arXiv · 2504.04151

STEP: Staged Parameter-Efficient Pre-training for Large Language Models

Kazuki Yano·Takumi Ito·Jun Suzuki

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Abstract

Pre-training large language models (LLMs) faces significant memory challenges due to the large size of model parameters. We introduce STaged parameter-Efficient Pre-training (STEP), which integrates parameter-efficient tuning techniques with model growth. We conduct experiments on pre-training LLMs of various sizes and demonstrate that STEP achieves up to a 53.9% reduction in maximum memory requirements compared to vanilla pre-training while maintaining equivalent performance. Furthermore, we show that the model by STEP performs comparably to vanilla pre-trained models on downstream tasks after instruction tuning.

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Kazuki Yano, Takumi Ito, Jun Suzuki. 2025-04-05. STEP: Staged Parameter-Efficient Pre-training for Large Language Models. https://arxiv.org/abs/2504.04151

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