Researchers argue computational depth is the missing scaling axis, claiming LLMs are depth-bottlenecked
burny_tech · x · 2026-09-23
Researchers argue computational depth is the missing scaling axis: params, data, sparsity, and test-time reasoning have all been scaled by orders of magnitude, but depth has stayed at 100 layers since GPT-3.
They claim LLMs are severely depth-bottlenecked and bad at using the depth they have, and that architectural interventions lifting this bottleneck yield gains that grow with compute.
Related event: Q Labs: Computational Depth Is the Missing Scaling Axis for LLMs(4 posts)→
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