Researchers Argue Computational Depth Is a Missing Scaling Axis for LLMs

A research team argues that computational depth is an overlooked scaling axis: while parameters, data, sparsity and test-time compute have grown by orders of magnitude, network depth has barely increased, leaving LLMs severely depth-limited.

2026-09-23 ~ 2026-09-23 · 2 related posts

• Researchers argue LLMs are severely depth-bottlenecked: the missing scaling axis — madhavsinghal_ · 2026-09-23
• Researchers argue computational depth is the missing scaling axis, claiming LLMs are depth-bottlenecked — burny_tech · 2026-09-23