LR Too Low
Learning rate too low causes slow convergence, plateau at high loss, or training to appear stuck.
Learning rate too low causes slow convergence, plateau at high loss, or training to appear stuck.
What this failure is
LR Too Low is a Training Stability failure seen during ML training runs. Learning rate too low causes slow convergence, plateau at high loss, or training to appear stuck. Common tags: Learning Rate, Too Low, Slow Convergence, Training Stability.
Is this what broke your run? Paste your log.
You're reading about LR Too Low. 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)
Learning rate too low for model. Adam epsilon too high. No learning rate warmup. Optimizer beta values wrong. 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 decreases very slowly
- Loss plateaus at high value
- Training seems stuck
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| Loss curve is flat | Learning rate too low for model |
| Validation accuracy is low | Adam epsilon too high |
| Training takes very long | No learning rate warmup |
Which systems are affected
- New model training
- Hyperparameter search
- Conservative training setup
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 curve is flat
- ✓Verified signal present: Validation accuracy is low
- ✓Verified signal present: Training takes very long
- ✓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
- Learning rate too low for model
- Adam epsilon too high
- No learning rate warmup
- Optimizer beta values wrong
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.