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

2025-04-05

Annual Meeting of the Association for Computational Linguistics

AI Summary

To address the prominent GPU memory bottleneck in large language model (LLM) pretraining, this paper proposes the Staged Parameter-Efficient Training (SPET) framework. SPET is the first to deeply integrate parameter-efficient fine-tuning techniques—such as LoRA—into the *entire* pretraining pipeline, synergistically combining gradient checkpointing with staged architectural expansion to enable dynamic model growth and on-demand memory optimization. Implemented in PyTorch, SPET introduces a memory-aware training scheduler that reduces peak GPU memory consumption by up to 53.9% versus full-parameter baselines, while preserving pretraining performance. Downstream task performance after instruction tuning remains unchanged. The core contribution lies in bridging the paradigmatic divide between standard pretraining and parameter-efficient adaptation, establishing a scalable, memory-efficient, and unified pretraining paradigm.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Computer Vision: Large Vision Models

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Reduces memory use in LLM pre-training
• Integrates efficient tuning with model growth
• Maintains performance with less memory

Innovation

• Staged parameter-efficient pre-training for LLMs
• Integrates efficient tuning with model growth
• Reduces memory by 53.9% with same performance