GLU Networks Outperform Non-Gated Counterparts Due to Favorable NTK Spectrum
A recent investigation published on arXiv (2605.20749) explains the superior performance of Gated Linear Units (GLU) compared to non-gated architectures in extensive language models. Researchers examined two-layer networks within the neural tangent kernel (NTK) framework and discovered that GLU alters the NTK spectrum, resulting in a reduced condition number and a denser eigenvalue distribution. This modification facilitates quicker convergence and a notable loss-crossing effect. Experiments conducted on ViT and GPT-2 indicate that the main advantage of GLU lies in enhancing optimization speed rather than minimizing the generalization gap.
Key facts
- GLU and variants are widely used in modern open-source LLM architectures.
- GLU consistently outperforms non-gated counterparts.
- Study analyzes two-layer networks in the NTK regime.
- GLU structure reshapes NTK spectrum with smaller condition number.
- Reshaped spectrum leads to faster convergence.
- Loss-crossing phenomenon observed between GLU and non-GLU models.
- GLU has limited impact on reducing generalization gap in ViT and GPT-2.
- Primary benefit of GLU is accelerating optimization.
Entities
Institutions
- arXiv