arXiv:2605.26895v1 Announce Type: new Abstract: Normalization layers in modern large language models (LLMs) consist of a deterministic normalization operation and a learnable scale vector. While the normalization operation has been extensively studied, the scale vector remains poorly understood despite its ubiquitous use. In this work, we present a systematic study of scale vectors in LLMs from the perspectives of expressivity, optimization, and architectural structure. First, we show empirically that although scale vectors constitute only a negligible fraction of model parameters, removing th
The rapid advancement and scaling of LLMs necessitate deeper understanding of their foundational components to optimize performance and efficiency.
Understanding the function of scale vectors, despite their small size, can lead to significant improvements in LLM architecture, training, and overall capability.
This research provides a more complete picture of LLM mechanics, potentially guiding future model design for greater expressivity and optimization.
- · AI researchers
- · LLM developers
- · AI-powered software companies
- · Inefficient LLM architectures
Improved performance and efficiency of large language models through better understanding of scale vectors.
Reduced computational costs and accelerated innovation in AI applications due to more optimized LLM designs.
Enhanced capabilities of AI agents and broader accessibility of advanced AI due to more efficient underlying models.
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