Gradient Clipping Missing
Missing gradient clipping causes gradient explosion in RNNs, transformers, and GANs.
Missing gradient clipping causes gradient explosion in RNNs, transformers, and GANs.
What this failure is
Gradient Clipping Missing is a Training Stability failure seen during ML training runs. Missing gradient clipping causes gradient explosion in RNNs, transformers, and GANs. Common tags: Gradient Clipping, Gradient Explosion, Training Stability, Transformer.
Is this what broke your run? Paste your log.
You're reading about Gradient Clipping Missing. Paste your own crash log or traceback below and get the real root cause for YOUR run, not this generic entry. No account, no card. Logs are masked at ingress and never saved to account history.
Want 14 days on the Scale plan?
Request an evaluation code. A verified workplace organization activates up to 50 diagnoses a day, alerts, history, and follow-up questions. No credit card or automatic subscription.
Why it happens (the mechanism)
Gradient norm unbounded. No clip_grad_norm_() call. Clip value too high to be effective. Clip applied to wrong optimizer. 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
- Training diverges with large gradients
- Loss spikes occasionally
- NaN loss from gradient explosion
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| Gradient norm grows very large | Gradient norm unbounded |
| Loss spike followed by NaN | No clip_grad_norm_() call |
| Model parameters oscillate wildly | Clip value too high to be effective |
Which systems are affected
- RNN/LSTM training
- Transformer training
- GAN training
- Training with high learning rate
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: Gradient norm grows very large
- ✓Verified signal present: Loss spike followed by NaN
- ✓Verified signal present: Model parameters oscillate wildly
- ✓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
The root cause is on this page and stays free. A free account adds the exact remediation steps, saved history, and the fix on every entry in the encyclopedia.
Sign up free. Unlock the full analysisNo credit card. Daily allowance follows verified trust tier. Instant access.
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.
Install the free VS Code extensionRelated failures to investigate next
Root cause
- Gradient norm unbounded
- No clip_grad_norm_() call
- Clip value too high to be effective
- Clip applied to wrong optimizer
The fix and how to prevent it
Evaluate Denpex on your own logs
Request a Scale evaluation code. A verified workplace organization activates 14 days with up to 50 diagnoses a day. Every account keeps its current diagnosis allowance and gets a verification path. No card or automatic subscription.
Don't just read the fix, diagnose your run
The encyclopedia tells you what went wrong. Denpex tells you what went wrong in YOUR training run. With your logs, your config, and your stack.