NaN Loss
NaN loss in training is critical because it propagates through all parameters and corrupts the model permanently.
NaN loss in training is critical because it propagates through all parameters and corrupts the model permanently.
What this failure is
NaN Loss is a Training Stability failure seen during ML training runs. NaN loss in training is critical because it propagates through all parameters and corrupts the model permanently. Common tags: Nan, Loss, Training Stability, Debugging.
Is this what broke your run? Paste your log.
You're reading about NaN Loss. 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 explosion. FP16 underflow/overflow. Bad data batch with extreme values. Division by zero in loss. Log(0) or sqrt(negative) in loss. 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 becomes NaN mid-training
- All model parameters become NaN
- Training can't recover from NaN
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| loss.item() returns nan | Gradient explosion |
| Model outputs are NaN | FP16 underflow/overflow |
| Gradients are NaN | Bad data batch with extreme values |
| Loss is inf | Division by zero in loss |
Which systems are affected
- Mixed precision training
- Training with high learning rate
- Training with unstable loss
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.item() returns nan
- ✓Verified signal present: Model outputs are NaN
- ✓Verified signal present: Gradients are NaN
- ✓Verified signal present: Loss is inf
- ✓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 explosion
- FP16 underflow/overflow
- Bad data batch with extreme values
- Division by zero in loss
- log(0) or sqrt(negative) in loss
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.