LR Too High
Learning rate too high causes training instability, loss divergence, or NaN loss.
Learning rate too high causes training instability, loss divergence, or NaN loss.
What this failure is
LR Too High is a Training Stability failure seen during ML training runs. Learning rate too high causes training instability, loss divergence, or NaN loss. Common tags: Learning Rate, Too High, Training Stability, Divergence.
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Why it happens (the mechanism)
Learning rate too high for model. No warmup before high LR. Adam epsilon too small. Gradient explosion at start. 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 explodes early in training
- Training diverges with NaN loss
- Model outputs are NaN or Inf
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| Loss is NaN after few iterations | Learning rate too high for model |
| Loss spike at start of training | No warmup before high LR |
| Model parameters become NaN | Adam epsilon too small |
Which systems are affected
- New model training
- Trying new architecture
- Hyperparameter search
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 is NaN after few iterations
- ✓Verified signal present: Loss spike at start of training
- ✓Verified signal present: Model parameters become NaN
- ✓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
Select the traceback or open the failed terminal, then run Denpex locally to see the initiating rank, collateral failures, exact fix, and verification command without uploading the log.
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Root cause
- Learning rate too high for model
- No warmup before high LR
- Adam epsilon too small
- Gradient explosion at start
The fix and how to prevent it
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Don't just read the fix, diagnose your run
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