Warm-Start Initialization Policy Impacts in Multi-Run Model Training

Abstract

This article examines the effect of warm-start initialization policies in multi-run model training,
focusing on how inherited parameter states shape convergence behavior, stability, and adaptability.
Warm-starting enables models to leverage previously learned representations, reducing training time
and improving consistency across repeated runs. The findings show that warm-start initialization
accelerates convergence and stabilizes performance outcomes, particularly in scenarios involving
iterative retraining or incremental data updates. However, the approach can also limit exploration of
alternative solution spaces and reduce flexibility when encountering shifts in data patterns. The study
highlights the balance required between efficiency and adaptability, emphasizing that warm-start
strategies are most effective when integrated into training workflows that monitor performance
plateauing, account for concept drift, and selectively reset learning states when necessary.