Factorizing attention matrices rewires gradient flow, destroying an information-exponent bottleneck

burny_tech · x · 2026-10-01

• The author argues that factorizing the attention matrix (weight tying, S = WWᵀ) is not merely a parameterization trick—it fundamentally rewires how gradients flow.
• Key claim: weight tying removes an information-exponent bottleneck that stalls unfactored models indefinitely, letting learning proceed where the unbound form gets stuck.
• The thread walks through the underlying math explaining why such tied/factored structures behave so differently during optimization.

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