A Layer Norm's Jacobian Is a Projector, So 27 Post-LN Blocks Attenuate the Gradient Once and by 0.6676, Not 27 Times
This story is from 2026-08-26. It is preserved in the archive; the latest stories are on the live feed.
This page was written to say that Post-LN decays the gradient geometrically with depth. The measurement killed it, and the reason is a line of algebra rather than a training run. A layer norm's Jacobian is 1/σ times an orthogonal projector , and a projector is idempotent: apply it once and the mean…
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- 2026-08-26 21:05 · DEV Community — Machine Learning
A Layer Norm's Jacobian Is a Projector, So 27 Post-LN Blocks Attenuate the Gradient Once and by 0.6676, Not 27 Times
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