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

• URL: http://arxiv.org/abs/2504.04151v1
• Date: Sat, 05 Apr 2025 12:07:08 GMT
• Title: STEP: Staged Parameter-Efficient Pre-training for Large Language Models
• Authors: Kazuki Yano, Takumi Ito, Jun Suzuki,
• Abstract summary: Pre-training large language models (LLMs) faces significant memory challenges due to the large size of model parameters.<n>We introduce STaged parameter-Efficient Pre-training (STEP), which integrates parameter-efficient tuning techniques with model growth.
• Score: 16.77087225406202
• License: http://creativecommons.org/licenses/by/4.0/
• 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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