SGD Momentum Configuration Issue
SGD momentum misconfiguration causes training to oscillate, diverge, or converge slowly.
SGD momentum misconfiguration causes training to oscillate, diverge, or converge slowly.
What this failure is
SGD Momentum Configuration Issue is a Training Stability failure seen during ML training runs. SGD momentum misconfiguration causes training to oscillate, diverge, or converge slowly. Common tags: Sgd, Momentum, Optimizer, Training Stability.
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Why it happens (the mechanism)
Momentum too high causes oscillation. Momentum too low causes slow convergence. Momentum without Nesterov slows training. Learning rate and momentum not balanced. Taken together, these mechanisms explain why the failure is reproducible, why it tends to surface on specific workloads or scales, and why generic mitigation attempts often fall short without addressing the underlying cause.
What you'll observe
- Loss oscillates wildly
- Loss diverges after some training
- Training is very slow to converge
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| Loss increases after warmup | Momentum too high causes oscillation |
| Loss oscillates with large amplitude | Momentum too low causes slow convergence |
| Training time exceeds expectations | Momentum without Nesterov slows training |
Which systems are affected
- SGD training with momentum
- Fine-tuning with SGD
- ResNet training with SGD
How to confirm this is the problem
Use this checklist to test the hypothesis against a small reproduction. No single line proves the root cause, so preserve the preceding events and compare one variable at a time.
- ✓Reproduce the failure from a clean checkpoint/seed: the symptom must appear without warm-up state from a previous run.
- ✓Verified signal present: Loss increases after warmup
- ✓Verified signal present: Loss oscillates with large amplitude
- ✓Verified signal present: Training time exceeds expectations
- ✓A targeted fix from the "How to fix it" section eliminates or substantially reduces the symptom within one validation pass.
The fix and the prevention pattern
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Diagnose this failure in VS Code
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Root cause
- Momentum too high causes oscillation
- Momentum too low causes slow convergence
- Momentum without Nesterov slows training
- Learning rate and momentum not balanced
The fix and how to prevent it
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