AnalysisAI ModelsNovember 13, 2026

Cohere session examines why self-repairing training converges without gradient updates

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Cohere's session argues standard gradient-descent convergence proofs assume every update is a gradient, an assumption self-repairing training breaks by design yet still converges. It demonstrates the case by zeroing out a converged convolutional layer at epoch 100 and pinpoints exactly where the proof stops holding.

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