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About this paper
Zhen Tan, Daize Dong, Xinyu Zhao, Jie Peng, Yu Cheng, Tianlong Chen
This paper presents Dynamic Layer Operation (DLO), a framework for efficient vertical scaling of Large Language Models that dynamically activates or skips layers based on input complexity. DLO achieves performance comparable to dense models with reduced computational costs, cutting training expenses by up to 90% by avoiding continual pre-training.
Key insights
- LLM layer representations show redundancy for many inputs, suggesting that not all layers are needed for every computation.
- Dynamic layer activation, inspired by human brain dynamics, allows for adaptive computation where more complex inputs utilize deeper neural pathways.
- Identity initialization for expanded layers is effective, enabling new layers to gradually learn transformations while initially preserving input features.
Summary by alphaXiv.
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