Discussion on Per-Head Scaling in Multi-Head Attention
stochasticchasm · x · 2026-07-16
This reply discusses a parameterization detail in multi-head attention:
- The other party noted that in many LLMs, qnorm is simply applied to qproj(x), and the q/k vectors of each head are not scaled by \sqrt(dhead).
- The question raised here is why we don't use "per head independent gain" (e.g., nn.RMSNorm((nhead, dhead), ...)) to achieve per head gain.
- The response suggests this is related to the muP concept.
- Another reply adds that explaining attention's 1/d scaling based on per-head qk normalization could itself be a novel idea.
Related event: Researchers Debate 1/d-Style Attention Scaling in Modern LLMs(6 posts)→
More from Research
- 3D ResNet Paper Crosses 3,000 Citations Eight Years After CVPR 2018 — HirokatuKataoka · 2026-09-11
- Jeff Heaton's Intro to the Math of Neural Networks eBook Is Free to Download — blaizedsouza · 2026-09-11
- Mathematician Daniel Litt Launches Problem Repo to Track Human vs AI Progress: 15 Problems, 1 Solved — littmath · 2026-09-11
- Open ECDSA.fail challenge uses AI agents to shrink Shor's-algorithm quantum circuits for Bitcoin keys — StefanoGogioso · 2026-09-11
- Alex Townsend posts 200 open problems in numerical linear algebra for humans and AI agents — IgorCarron · 2026-09-11
- Navier-Stokes, Riemann, P vs NP: what this week's math buzzwords mean for you — koltregaskes · 2026-09-11