GQA-{\mu}P: The maximal parameterization update for grouped query attention
The paper introduces GQA-μP, a method for maximizing parameterization updates in grouped query attention. It highlights advances in hyperparameter transfer across model architectures, which can reduce the computational burden of tuning large language models. The authors provide theoretical derivations and experimental results demonstrating the effectiveness of their approach.
- ▪Hyperparameter transfer can significantly reduce the compute needed for tuning large language models.
- ▪The maximal update parameterization ensures transfer through mathematical analysis but is difficult to derive for new architectures.
- ▪The authors present a modified spectral norm that maintains valid scaling laws for network weights.
arXiv cs.AI files mainly under ai research. We currently carry 1,128 of its stories.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Machine Learning arXiv:2605.15290 (cs) [Submitted on 14 May 2026] Title:GQA-μP: The maximal parameterization update for grouped query attention Authors:Kyle R. Chickering, Huijuan Wang, Mengxi Wu, Alexander Moreno, Muhao Chen, Xuezhe Ma, Daria Soboleva, Joel Hestness, Zhengzhong Liu, Eric Xing View a PDF of the paper titled GQA-{\mu}P: The maximal parameterization update for grouped query attention, by Kyle R. Chickering and 9 other authors View PDF HTML (experimental) Abstract:Hyperparameter transfer across model architectures dramatically reduces the amount of compute necessary for tuning large language models (LLMs).
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
Discussion
0 commentsMore from arXiv cs.AI
