weight400continues

Weight400continues is a term used in discussions of iterative optimization and neural network training to denote a continuation strategy for weight updates from a baseline state associated with a factor labeled '400'. The term is informal and appears in technical forums, notes, and some preprint discussions rather than in formal standards.

Conceptually, weight400continues describes a two-phase optimization: an initial phase where weights are learned under a standard regime, followed by a continuation phase in which the weight vector is further updated with a constrained schedule that preserves elements deemed important while allowing others to adapt. The '400' designation may reference a scaling factor, a memory budget, or epoch indexing used by practitioners to distinguish this continuation from other schedules.

Implementation commonly involves adjusting hyperparameters such as learning rate, weight decay, or pruning thresholds at a defined transition point. The continuation phase aims to reduce catastrophic forgetting in transfer learning or continual learning settings by maintaining a core weight configuration while permitting peripheral weights to adapt.

Applications include fine-tuning large neural models, continual learning experiments, and scenarios where reproducibility across training runs is valued. Reported benefits in informal discussions include improved stability and smoother loss trajectories, though empirical evidence is limited and results vary by architecture and dataset.

Critiques focus on the lack of formal definition, inconsistent naming, and the challenge of isolating its effects from other regularization and scheduling methods. Related concepts include weight decay, learning rate schedules, and elastic weight consolidation, a well-known technique in continual learning.