# Training Instability > [!metadata]- Metadata > **Published:** [[2025-02-09|Feb 09, 2025]] > **Tags:** #🌐 #learning-in-public #artificial-intelligence #ethical-ai #bias-mitigation Training instability is a significant challenge in [[Adversarial Debiasing]] where achieving balance between the predictor and adversary models becomes difficult during the training process. ## Key Aspects 1. **Convergence Issues**: - Models may fail to reach a stable state - Training can oscillate between different states - May never achieve optimal performance 2. **Opposing Objectives**: - Predictor aims to minimize its loss - Adversary attempts to maximize its effectiveness - These conflicting goals create training dynamics challenges ## Causes 1. **Complex Interactions**: - Interdependence between predictor and adversary - Non-linear relationships in model behaviors - Sensitivity to hyperparameter choices 2. **Optimization Challenges**: - Difficulty in finding equilibrium between models - Potential for mode collapse - Gradient instability issues ## Impact on Debiasing Training instability can affect debiasing efforts by: - Reducing model reliability - Creating inconsistent performance - Making it harder to achieve fairness goals ## Mitigation Strategies 1. **Careful Hyperparameter Tuning**: - Balanced learning rates - Appropriate batch sizes - Proper model architecture selection 2. **Advanced Training Techniques**: - Gradient penalty methods - Progressive training approaches - Regularization strategies [Learn more about training instability in adversarial models](@https://dl.acm.org/doi/pdf/10.1145/3278721.3278779)