One-Cycle Policy Issue
One-cycle policy issues arise from incorrect max_lr, momentum range, or training duration that destabilize training.
One-cycle policy issues arise from incorrect max_lr, momentum range, or training duration that destabilize training.
What this failure is
One-Cycle Policy Issue is a Training Stability failure seen during ML training runs. One-cycle policy issues arise from incorrect max_lr, momentum range, or training duration that destabilize training. Common tags: One Cycle, Super Convergence, Scheduler, Momentum.
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Why it happens (the mechanism)
Max LR too high for one-cycle. Momentum range inverted. Total steps miscalculated. Anneal strategy doesn't match scheduler. 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
- One-cycle policy causes loss spikes
- One-cycle model underperforms constant LR
- One-cycle training is unstable at peak
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| Loss spikes at peak LR | Max LR too high for one-cycle |
| Training is unstable in second half | Momentum range inverted |
| One-cycle model doesn't converge | Total steps miscalculated |
Which systems are affected
- Super-convergence training with one-cycle
- Fast training with one-cycle policy
- Image classification with one-cycle
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 spikes at peak LR
- ✓Verified signal present: Training is unstable in second half
- ✓Verified signal present: One-cycle model doesn't converge
- ✓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
- Max LR too high for one-cycle
- Momentum range inverted
- Total steps miscalculated
- Anneal strategy doesn't match scheduler
The fix and how to prevent it
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