Add a slowly updated adversarial sampler over training contexts, domain shifts, perturbation levels, or task instances. The neural network trains normally on samples from the current mixture, while a contextual bandit increases probability on contexts with high recent validation loss or catastrophic constraint violation. Unlike static domain randomization, this curriculum explicitly targets current failure modes without changing the model architecture.
Introduce two bounded state variables into training: x measures latent, reliable learning progress, while y measures the currently active population of high-gain parameter updates or difficult examples. Let x increase irreversibly when active updates are productive, while y grows through interaction with the latent pool and decays through exhaustion. Use y to gate the learning rate or curriculum intensity, producing a low-noise incubation phase followed by an endogenous acceleration phase once…
Represent augmentation centers or training examples in a low-dimensional torus and accumulate the geometric overlap of their augmentation neighborhoods. Add a finite-horizon penalty that detects latent locations with insufficient accumulated coverage, while constraining center uniformity so that coverage optimization does not collapse all samples to one location.