Training Stability

Training stability refers to how smoothly and reliably a machine learning model learns during its training process. When training is stable, the model's performance improves steadily without erratic fluctuations or sudden setbacks. Instability can cause the model to produce inconsistent results or "weird" outputs, making it hard to trust or deploy. Factors like proper learning rates, good data quality, and effective algorithms help ensure training stays stable. In essence, stable training means the model develops as expected, leading to accurate and reliable performance once fully trained.