LR Without Warmup or Decay
Training without learning rate warmup or decay causes slow convergence and poor final performance.
Training without learning rate warmup or decay causes slow convergence and poor final performance.
What this failure is
LR Without Warmup or Decay is a Training Stability failure seen during ML training runs. Training without learning rate warmup or decay causes slow convergence and poor final performance. Common tags: Learning Rate, Warmup, Decay, Scheduler.
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Why it happens (the mechanism)
Constant LR is too high causing oscillation. Constant LR is too low causing slow convergence. No warmup causes initial loss spikes. No decay means final LR is same as initial. 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 converges slowly
- Final loss is higher than expected
- Training is unstable at start
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| Loss decreases very slowly | Constant LR is too high causing oscillation |
| Loss jumps at the start of training | Constant LR is too low causing slow convergence |
| Final loss is higher than with proper schedule | No warmup causes initial loss spikes |
Which systems are affected
- Training from scratch with constant LR
- Large model training
- Training with no LR schedule
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 decreases very slowly
- ✓Verified signal present: Loss jumps at the start of training
- ✓Verified signal present: Final loss is higher than with proper schedule
- ✓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
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Root cause
- Constant LR is too high causing oscillation
- Constant LR is too low causing slow convergence
- No warmup causes initial loss spikes
- No decay means final LR is same as initial
The fix and how to prevent it
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