Optimizer ideas

Projection-Regularized Gradient Updates

Replace unconstrained neural-network updates by updates projected toward directions supported by a recent, regularized gradient or feature subspace. This transfers PRPC's errors-in-variables correction: directions that are weakly identified by noisy or rank-deficient minibatches receive stronger shrinkage, preventing large updates caused by accidental correlations. The method is especially suitable for recurrent, world-model, and small-data fine-tuning problems where minibatch covariance is…