Using Training Optimization

Overview

This meta-skill routes you to the right training optimization specialist based on symptoms. Training issues often have multiple potential causes—this skill helps diagnose symptoms and route to the appropriate specialist. Load this skill when you encounter training problems but aren't sure which specific technique to apply.

Core Principle: Diagnose before routing. Training issues often have multiple causes. Ask clarifying questions to understand symptoms before routing to specific skills. Wrong diagnosis wastes time—systematic routing saves it.

When to Use

Load this skill when:

• Model not learning (loss stuck, not decreasing, poor accuracy)
• Training instability (loss spikes, NaN values, divergence)
• Overfitting (large train/val gap, poor generalization)
• Training too slow (throughput issues, time constraints)
• Hyperparameter selection (optimizer, learning rate, batch size, regularization)
• Experiment management (tracking runs, comparing configurations)
• Convergence issues (slow learning, plateaus, local minima)
• Setting up new training pipeline