Stochastic Gradient Flooding Effects in Long-Horizon Model Training

Abstract

This article investigates stochastic gradient flooding phenomena in long-horizon model training,
examining how optimization behavior shifts as training progresses beyond typical convergence
phases. Using controlled training pipelines and extended iteration schedules, the study analyzes the
transition from coherent gradient descent to noise-dominated update patterns that lead to instability
and representational collapse. Results reveal that gradient flooding is not simply a numerical artifact
but a structural effect tied to the interaction between model depth, temporal dependency, and
diminishing curvature in the loss landscape. Early training cycles exhibit meaningful learning signals,
while later stages produce volatile gradient magnitudes that distort parameter space geometry and
degrade generalization performance. Mitigation strategies including gradient clipping, adaptive decay
scheduling, and selective layer reinitialization were tested, with structural stabilization approaches
proving more effective than magnitude suppression alone. These findings highlight the need for
horizon-aware training methodologies that preserve representation integrity and maintain controlled
parameter evolution throughout long-duration optimization.