using-training-optimization

# 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

**Don't use for**: PyTorch implementation bugs (use pytorch-engineering), model architecture selection (use neural-architectures), production deployment (use ml-production), RL-specific training (use deep-rl), LLM fine-tuning specifics (use llm-specialist)

---

## How to Access Reference Sheets

**IMPORTANT**: All reference sheets are located in the SAME DIRECTORY as this SKILL.md file.

When this skill is loaded from:
  `skills/using-training-optimization/SKILL.md`

Reference sheets like `optimization-algorithms.md` are at:
  `skills/using-training-optimization/optimization-algorithms.md`

NOT at:
  `skills/optimization-algorithms.md` ← WRONG PATH

When you see a link like `[optimization-algorithms.md](optimization-algorithms.md)`, read the file from the same directory as this SKILL.md.

---

## Routing by Primary Symptom

### Symptom: "Model Not Learning" / "Lo