Langford argues gradient descent pushes transformers to discard deep-layer information
JohnCLangford · x · 2026-09-06
- John Langford threads an analysis of what gradient descent implies for transformer architecture: early layers handle token sense-making, while later layers may drop computed information once the next-token distribution is found.
- @hiveecho argues carrying computed state across timesteps risks recurrent state drift, and discarding transient states may enable self-correction by reducing path dependence.
- Langford contrasts a Full Bandwidth Transformer: deep compute in early layers, information preserved in final layers, better depth utilization, and gradients free to quench prior-token info.
Related event: Langford Explores Information Dropping in Transformer Architectures(3 posts)→
More from Models
- Testing AI models on games is a mess: famous games leak guides, obscure games bore everyone — banteg · 2026-09-06
- Should Models Be Trained on Certain Games? Models May Be 'Too Smart' to Play — scaling01 · 2026-09-06
- 'LLM Psychosis' Reframed: Models Mistaking Their Own Thoughts for Injected Ones — paul_cal · 2026-09-06
- Ethan Mollick: fable 5.1 and astra exceed routine knowledge work — expect amplification — emax · 2026-09-06
- GPT-6 Astra users hit frequent 'model at capacity' errors — bytebot · 2026-09-06
- Astra User Says Weekly Token Limits Too Tight: 24-Hour Run Still Unfinished — Wooden_Drag9473 · 2026-09-06