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)

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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.

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Routing by Primary Symptom

Symptom: "Model Not Learning" / "Loss Not Decreasing"

Keywords: stuck, flat loss, not improving, not learning, accuracy not increasing

Diagnostic questions (ask BEFORE routing):

1. "Is loss completely flat from the start, decreasing very slowly, or was it learning then stopped?"
2. "Any NaN or Inf values in your loss?"
3. "What optimizer and learning rate are you using?"
4. "What does your loss curve look like over time?"

Route based on answers:

Loss Behavior	Likely Cause	Route To	Why
Flat from epoch 0	LR too low OR wrong optimizer OR inappropriate loss	learning-rate-scheduling + optimization-algorithms	Need to diagnose starting conditions
Was learning, then plateaued	Local minima OR LR too high OR overfitting	learning-rate-scheduling (scheduler) + check validation loss for overfitting	Adaptation needed during training
Oscillating wildly	LR too high OR gradient instability	learning-rate-scheduling + gradient-management	Instability issues
NaN or Inf	Gradient explosion OR numerical instability	gradient-management (PRIMARY) + loss-functions-and-objectives	Stability critical
Loss function doesn't match task	Wrong objective	loss-functions-and-objectives	Fundamental mismatch

Multi-skill routing: Often need optimization-algorithms (choose optimizer) + learning-rate-scheduling (choose LR/schedule) together for "not learning" issues.

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Symptom: "Training Unstable" / "Loss Spikes" / "NaN Values"

Keywords: NaN, Inf, exploding, diverging, unstable, spikes

Diagnostic questions:

1. "When do NaN values appear - immediately at start, or after N epochs?"
2. "What's your learning rate and schedule?"
3. "Are you using mixed precision training?"
4. "What loss function are you using?"

Route to (in priority order):

1. gradient-management (PRIMARY)

   • When: Gradient explosion, NaN gradients
   • Why: Gradient issues cause instability. Must stabilize gradients first.
   • Techniques: Gradient clipping, gradient scaling, NaN debugging

2. learning-rate-scheduling (SECONDARY)

   • When: LR too high causes instability
   • Why: High LR can cause divergence even with clipped gradients
   • Check: If NaN appears later in training, LR schedule might be increasing too much

3. loss-functions-and-objectives (if numerical issues)

   • When: Loss computation has numerical instability (log(0), division by zero)
   • Why: Numerical instability in loss propagates to gradients
   • Check: Custom loss functions especially prone to this

Cross-pack note: If using mixed precision (AMP), also check pytorch-engineering (mixed-precision-and-optimization) for gradient scaling issues.

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Symptom: "Model Overfits" / "Train/Val Gap Large"

Keywords: overfitting, train/val gap, poor generalization, memorizing training data

Diagnostic questions:

1. "How large is your dataset (number of training examples)?"
2. "What's the train accuracy vs validation accuracy?"
3. "What regularization are you currently using?"
4. "What's your model size (parameters) relative to dataset size?"

Route to (multi-skill approach):

1. overfitting-prevention (PRIMARY)

   • Techniques: Dropout, weight decay, early stopping, L1/L2 regularization
   • When: Always the first stop for overfitting
   • Why: Comprehensive regularization strategy

2. data-augmentation-strategies (HIGHLY RECOMMENDED)

   • When: Dataset is small (< 10K examples) or moderate (10K-100K)
   • Why: Increases effective dataset size, teaches invariances
   • Priority: Higher priority for smaller datasets

3. hyperparameter-tuning

   • When: Need to find optimal regularization strength
   • Why: Balance between underfitting and overfitting
   • Use: After implementing regularization techniques

Decision factors:

• Small dataset (< 1K): data-augmentation is CRITICAL + overfitting-prevention
• Medium dataset (1K-10K): overfitting-prevention + data-augmentation
• Large dataset (> 10K) but still overfitting: overfitting-prevention + check model capacity
• Model too large: Consider neural-architectures for model size discussion

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Symptom: "Training Too Slow" / "Low Throughput"

Keywords: slow training, low GPU utilization, takes too long, time per epoch

CRITICAL: Diagnose bottleneck before routing

Diagnostic questions:

1. "Is it slow per-step (low throughput) or just need many steps?"
2. "What's your GPU utilization percentage?"
3. "Are you using data augmentation? How heavy?"
4. "What's your current batch size?"

Route based on bottleneck:

GPU Utilization	Likely Cause	Route To	Why
< 50% consistently	Data loading bottleneck OR CPU preprocessing	pytorch-engineering (data loading, profiling)	Not compute-bound, infrastructure issue
High (> 80%) but still slow	Batch size too small OR need distributed training	batch-size-and-memory-tradeoffs + possibly pytorch-engineering (distributed)	Compute-bound, need scaling
High + heavy augmentation	Augmentation overhead	data-augmentation-strategies (optimization) + pytorch-engineering (profiling)	Augmentation CPU cost
Memory-limited batch size	Can't increase batch size due to OOM	batch-size-and-memory-tradeoffs (gradient accumulation) + pytorch-engineering (memory)	Memory constraints limiting throughput

Cross-pack boundaries:

• Data loading issues → pytorch-engineering (DataLoader, prefetching, num_workers)
• Distributed training setup → pytorch-engineering (distributed-training-strategies)
• Batch size optimization for speed/memory → training-optimization (batch-size-and-memory-tradeoffs)
• Profiling to identify bottleneck → pytorch-engineering (performance-profiling)

Key principle: Profile FIRST before optimizing. Low GPU utilization = wrong optimization target.

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Symptom: "Which X Should I Use?" (Direct Questions)

Direct hyperparameter questions route to specific skills:

Question	Route To	Examples
"Which optimizer?"	optimization-algorithms	SGD vs Adam vs AdamW, momentum, betas
"Which learning rate?"	learning-rate-scheduling	Initial LR, warmup, schedule type
"Which batch size?"	batch-size-and-memory-tradeoffs	Batch size effects on convergence and speed
"Which loss function?"	loss-functions-and-objectives	Cross-entropy vs focal vs custom
"How to prevent overfitting?"	overfitting-prevention	Dropout, weight decay, early stopping
"Which augmentation?"	data-augmentation-strategies	Type and strength of augmentation
"How to tune hyperparameters?"	hyperparameter-tuning	Search strategies, AutoML
"How to track experiments?"	experiment-tracking	MLflow, W&B, TensorBoard

For new project setup, route to MULTIPLE in sequence:

1. optimization-algorithms - Choose optimizer
2. learning-rate-scheduling - Choose initial LR and schedule
3. batch-size-and-memory-tradeoffs - Choose batch size
4. experiment-tracking - Set up tracking
5. training-loop-architecture - Design training loop

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Symptom: "Need to Track Experiments" / "Compare Configurations"

Keywords: experiment tracking, MLflow, wandb, tensorboard, compare runs, log metrics

Route to:

1. experiment-tracking (PRIMARY)

   • Tools: MLflow, Weights & Biases, TensorBoard, Neptune
   • When: Setting up tracking, comparing runs, organizing experiments
   • Why: Systematic experiment management

2. hyperparameter-tuning (if systematic search)

   • When: Running many configurations systematically
   • Why: Automated hyperparameter search integrates with tracking
   • Combined: Hyperparameter search + experiment tracking workflow

3. training-loop-architecture (for integration)

   • When: Need to integrate tracking into training loop
   • Why: Proper callback and logging design
   • Combined: Training loop + tracking integration

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Cross-Cutting Multi-Skill Scenarios

Scenario: New Training Setup (First Time)

Route to (in order):

1. optimization-algorithms - Select optimizer for your task
2. learning-rate-scheduling - Choose LR and warmup strategy
3. batch-size-and-memory-tradeoffs - Determine batch size
4. loss-functions-and-objectives - Verify loss function appropriate for task
5. experiment-tracking - Set up experiment logging
6. training-loop-architecture - Design training loop with checkpointing

Why this order: Foundation (optimizer/LR/batch) → Objective (loss) → Infrastructure (tracking/loop)

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Scenario: Convergence Issues

Route to (diagnose first, then in order):

1. gradient-management - Check gradients not vanishing/exploding (use gradient checking)
2. learning-rate-scheduling - Adjust LR schedule (might be too high/low/wrong schedule)
3. optimization-algorithms - Consider different optimizer if current one unsuitable

Why this order: Stability (gradients) → Adaptation (LR) → Algorithm (optimizer)

Common mistakes:

• Jumping to change optimizer without checking gradients
• Blaming optimizer when LR is the issue
• Not using gradient monitoring to diagnose

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Scenario: Overfitting Issues

Route to (multi-pronged approach):

1. overfitting-prevention - Implement regularization (dropout, weight decay, early stopping)
2. data-augmentation-strategies - Increase effective dataset size
3. hyperparameter-tuning - Find optimal regularization strength

Why all three: Overfitting needs comprehensive strategy, not single technique.

Prioritization:

• Small dataset (< 1K): data-augmentation is MOST critical
• Medium dataset (1K-10K): overfitting-prevention + augmentation balanced
• Large dataset but still overfitting: overfitting-prevention + check model size

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Scenario: Training Speed + Memory Constraints

Route to:

1. batch-size-and-memory-tradeoffs (PRIMARY) - Gradient accumulation to simulate larger batch
2. pytorch-engineering/tensor-operations-and-memory - Memory profiling and optimization
3. data-augmentation-strategies - Reduce augmentation overhead if bottleneck

Why: Speed and memory often coupled—need to balance batch size, memory, and throughput.

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Scenario: Multi-Task Learning or Custom Loss

Route to:

1. loss-functions-and-objectives (PRIMARY) - Multi-task loss design, uncertainty weighting
2. gradient-management - Check gradients per task, gradient balancing
3. hyperparameter-tuning - Tune task weights, loss coefficients

Why: Custom losses need careful design + gradient analysis + tuning.

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Ambiguous Queries - Clarification Protocol

When symptom unclear, ASK ONE diagnostic question before routing:

Vague Query	Clarifying Question	Why
"Fix my training"	"What specific issue? Not learning? Unstable? Overfitting? Too slow?"	4+ different routing paths
"Improve model"	"Improve what? Training speed? Accuracy? Generalization?"	Different optimization targets
"Training not working well"	"What's 'not working'? Loss behavior? Accuracy? Convergence speed?"	Need specific symptoms
"Optimize hyperparameters"	"Which hyperparameters? All of them? Specific ones like LR?"	Specific vs broad search
"Model performs poorly"	"Training accuracy poor or validation accuracy poor or both?"	Underfitting vs overfitting

Never guess when ambiguous. Ask once, route accurately.

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Common Routing Mistakes

Symptom	Wrong Route	Correct Route	Why
"Training slow"	batch-size-and-memory	ASK: Check GPU utilization first	Might be data loading, not compute
"Not learning"	optimization-algorithms	ASK: Diagnose loss behavior	Could be LR, gradients, loss function
"Loss NaN"	learning-rate-scheduling	gradient-management FIRST	Gradient explosion most common cause
"Overfitting"	overfitting-prevention only	overfitting-prevention + data-augmentation	Need multi-pronged approach
"Need to speed up training"	optimization-algorithms	Profile first (pytorch-engineering)	Don't optimize without measuring
"Which optimizer for transformer"	neural-architectures	optimization-algorithms	Optimizer choice, not architecture

Key principle: Diagnosis before solutions, clarification before routing, multi-skill for complex issues.

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When NOT to Use Training-Optimization Pack

Skip training-optimization when:

Symptom	Wrong Pack	Correct Pack	Why
"CUDA out of memory"	training-optimization	pytorch-engineering	Infrastructure issue, not training algorithm
"DDP not working"	training-optimization	pytorch-engineering	Distributed setup, not hyperparameters
"Which architecture to use"	training-optimization	neural-architectures	Architecture choice precedes training
"Model won't load"	training-optimization	pytorch-engineering	Checkpointing/serialization issue
"Inference too slow"	training-optimization	ml-production	Production optimization, not training
"How to deploy model"	training-optimization	ml-production	Deployment concern
"RL exploration issues"	training-optimization	deep-rl	RL-specific training concern
"RLHF for LLM"	training-optimization	llm-specialist	LLM-specific technique

Training-optimization pack is for: Framework-agnostic training algorithms, hyperparameters, optimization techniques, and training strategies that apply across architectures.

Boundaries:

• PyTorch implementation/infrastructure → pytorch-engineering
• Architecture selection → neural-architectures
• Production/inference → ml-production
• RL-specific algorithms → deep-rl
• LLM-specific techniques → llm-specialist

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Red Flags - Stop and Reconsider

If you catch yourself about to:

• ❌ Suggest specific optimizer without routing → Route to optimization-algorithms
• ❌ Suggest learning rate value without diagnosis → Route to learning-rate-scheduling
• ❌ Say "add dropout" without comprehensive strategy → Route to overfitting-prevention
• ❌ Suggest "reduce batch size" without profiling → Check if memory or speed issue, route appropriately
• ❌ Give trial-and-error fixes for NaN → Route to gradient-management for systematic debugging
• ❌ Provide generic training advice → Identify specific symptom and route to specialist
• ❌ Accept user's self-diagnosis without verification → Ask diagnostic questions first

All of these mean: You're about to give incomplete advice. Route to specialist instead.

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Common Rationalizations (Don't Do These)

Rationalization	Reality	What To Do Instead
"User is rushed, skip diagnostic questions"	Diagnosis takes 30 seconds, wrong route wastes 10+ minutes	Ask ONE quick diagnostic question: "Is loss flat, oscillating, or NaN?"
"Symptoms are obvious, route immediately"	Symptoms often have multiple causes	Ask clarifying question to eliminate ambiguity
"User suggested optimizer change"	User self-diagnosis can be wrong	"What loss behavior are you seeing?" to verify root cause
"Expert user doesn't need routing"	Expert users benefit from specialist skills too	Route based on symptoms, not user sophistication
"Just a quick question"	Quick questions deserve correct answers	Route to specialist—they have quick diagnostics too
"Single solution will fix it"	Training issues often multi-causal	Consider multi-skill routing for complex symptoms
"Time pressure means guess quickly"	Wrong guess wastes MORE time	Fast systematic diagnosis faster than trial-and-error
"They already tried X"	Maybe tried X wrong or X wasn't the issue	Route to specialist to verify X was done correctly
"Too complex to route"	Complex issues need specialists MORE	Use multi-skill routing for complex scenarios
"Direct answer is helpful"	Wrong direct answer wastes time	Routing IS the helpful answer

If you catch yourself thinking ANY of these, STOP and route to specialist or ask diagnostic question.

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Pressure Resistance - Critical Discipline

Time/Emergency Pressure

Pressure	Wrong Response	Correct Response
"Demo tomorrow, need quick fix"	Give untested suggestions	"Fast systematic diagnosis ensures demo success: [question]"
"Production training failing"	Panic and guess	"Quick clarification prevents longer outage: [question]"
"Just tell me which optimizer"	"Use Adam"	"30-second clarification ensures right choice: [question]"

Emergency protocol: Fast clarification (30 sec) → Correct routing (60 sec) → Specialist handles efficiently

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Authority/Hierarchy Pressure

Pressure	Wrong Response	Correct Response
"Senior said use SGD"	Accept without verification	"To apply SGD effectively, let me verify the symptoms: [question]"
"PM wants optimizer change"	Change without diagnosis	"Let's diagnose to ensure optimizer is the issue: [question]"

Authority protocol: Acknowledge → Verify symptoms → Route based on evidence, not opinion

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User Self-Diagnosis Pressure

Pressure	Wrong Response	Correct Response
"I think it's the optimizer"	Discuss optimizer choice	"What loss behavior makes you think optimizer? [diagnostic]"
"Obviously need to clip gradients"	Implement clipping	"What symptoms suggest gradient issues? [verify]"

Verification protocol: User attribution is hypothesis, not diagnosis. Verify with symptoms.

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Red Flags Checklist - Self-Check Before Routing

Before giving ANY training advice or routing, ask yourself:

1. ❓ Did I identify specific symptoms?

   • If no → Ask clarifying question
   • If yes → Proceed

2. ❓ Is this symptom in my routing table?

   • If yes → Route to specialist skill
   • If no → Ask diagnostic question

3. ❓ Am I about to give direct advice?

   • If yes → STOP. Why am I not routing?
   • Check rationalization table—am I making excuses?

4. ❓ Could this symptom have multiple causes?

   • If yes → Ask diagnostic question to narrow down
   • If no → Route confidently

5. ❓ Is this training-optimization or another pack?

   • PyTorch errors → pytorch-engineering
   • Architecture choice → neural-architectures
   • Deployment → ml-production

6. ❓ Am I feeling pressure to skip routing?

   • Time pressure → Route anyway (faster overall)
   • Authority pressure → Verify symptoms anyway
   • User self-diagnosis → Confirm with questions anyway
   • Expert user → Route anyway (specialists help experts too)

If you failed ANY check, do NOT give direct advice. Route to specialist or ask clarifying question.

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Training Optimization Specialist Skills

After routing, load the appropriate specialist skill for detailed guidance:

1. optimization-algorithms.md - Optimizer selection (SGD, Adam, AdamW, momentum), hyperparameter tuning, optimizer comparison
2. learning-rate-scheduling.md - LR schedulers (step, cosine, exponential), warmup strategies, cyclical learning rates
3. loss-functions-and-objectives.md - Custom losses, multi-task learning, weighted objectives, numerical stability
4. gradient-management.md - Gradient clipping, accumulation, scaling, vanishing/exploding gradient diagnosis
5. batch-size-and-memory-tradeoffs.md - Batch size effects on convergence, gradient accumulation, memory optimization
6. data-augmentation-strategies.md - Augmentation techniques (geometric, color, mixing), policy design, AutoAugment
7. overfitting-prevention.md - Regularization (L1/L2, dropout, weight decay), early stopping, generalization techniques
8. training-loop-architecture.md - Training loop design, monitoring, logging, checkpointing integration, callbacks
9. hyperparameter-tuning.md - Search strategies (grid, random, Bayesian), AutoML, Optuna, Ray Tune
10. experiment-tracking.md - MLflow, Weights & Biases, TensorBoard, Neptune, run comparison

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Quick Reference: Symptom → Skills

Symptom	Primary Skill	Secondary Skills	Diagnostic Question
Loss flat/stuck	learning-rate-scheduling	optimization-algorithms	"Flat from start or plateaued later?"
Loss NaN/Inf	gradient-management	learning-rate-scheduling, loss-functions	"When does NaN appear?"
Overfitting	overfitting-prevention	data-augmentation, hyperparameter-tuning	"Dataset size? Current regularization?"
Training slow	batch-size-and-memory OR pytorch-engineering	data-augmentation	"GPU utilization percentage?"
Oscillating loss	learning-rate-scheduling	gradient-management	"What's your current LR?"
Which optimizer	optimization-algorithms	learning-rate-scheduling	Task type, architecture, dataset
Which LR	learning-rate-scheduling	optimization-algorithms	Optimizer, task, current symptoms
Track experiments	experiment-tracking	hyperparameter-tuning, training-loop	Tools preference, scale of experiments
Poor generalization	overfitting-prevention	data-augmentation	Train vs val accuracy gap
Convergence issues	gradient-management	learning-rate-scheduling, optimization-algorithms	Gradient norms, loss curve

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Integration Notes

Cross-pack boundaries: pytorch-engineering (infrastructure), neural-architectures (architecture selection), deep-rl (RL-specific), llm-specialist (LLM-specific), ml-production (deployment).

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Summary

This meta-skill: Diagnose symptoms → Route to specialists → Resist pressure to give direct advice. Training issues often have multiple causes. Clarify symptoms, route to specialists. Wrong routing wastes more time than asking one clarifying question. Route based on symptoms, not guesses.