Modular TTT expresses test-time training as a graph of primitives, ablates the components, and finds that simple shallow learners with small learning-rate initialization and scalar decay match Gated DeltaNet at 1.45B scale.
Implicit regularization in deep matrix factorization
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Modular TTT: Rethinking Test-Time Training as Composable Modules
Modular TTT expresses test-time training as a graph of primitives, ablates the components, and finds that simple shallow learners with small learning-rate initialization and scalar decay match Gated DeltaNet at 1.45B scale.