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The ML Training Optimization Router serves as a specialized diagnostic hub for troubleshooting deep learning training pipelines. It analyzes common symptoms—such as flat loss curves, gradient instability (NaN values), overfitting, and low GPU throughput—to recommend the most effective optimization techniques. By providing structured diagnostic questions and systematic routing to specialized sub-skills like gradient management, learning rate scheduling, or data augmentation, it ensures developers apply the right fixes to complex convergence and performance problems without wasting time on trial and error.
