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Divergence choice in Shampoo-like optimizers fundamentally alters Kronecker approximation and finite-sample error compensation, explaining observed training behavior.
Sub-quadratic linear attention no longer requires full-precision state caching: 8-bit recurrent states can match FP32 accuracy while delivering up to 3.70× kernel speedups without any retraining.
