EMA Decay Too High
EMA (Exponential Moving Average) decay values that are too high or too low cause poor model averaging.
EMA (Exponential Moving Average) decay values that are too high or too low cause poor model averaging.
What this failure is
EMA Decay Too High is a Training Stability failure seen during ML training runs. EMA (Exponential Moving Average) decay values that are too high or too low cause poor model averaging. Common tags: Ema, Exponential Moving Average, Model Averaging, Training Stability.
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Why it happens (the mechanism)
EMA decay too high (e.g., 0.9999) means EMA updates too slowly. EMA decay too low (e.g., 0.9) means EMA tracks noisy training. EMA momentum not tuned for training duration. EMA not applied to all parameters. 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
- EMA model is too slow to track training model
- EMA model is too noisy
- EMA model diverges from training model
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| EMA model parameters are very different from training model | EMA decay too high (e.g., 0.9999) means EMA updates too slowly |
| EMA model doesn't improve generalization | EMA decay too low (e.g., 0.9) means EMA tracks noisy training |
| EMA model is identical to initial model | EMA momentum not tuned for training duration |
Which systems are affected
- Training with EMA for better generalization
- Fine-tuning with EMA
- Large model training with weight averaging
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: EMA model parameters are very different from training model
- ✓Verified signal present: EMA model doesn't improve generalization
- ✓Verified signal present: EMA model is identical to initial model
- ✓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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Root cause
- EMA decay too high (e.g., 0.9999) means EMA updates too slowly
- EMA decay too low (e.g., 0.9) means EMA tracks noisy training
- EMA momentum not tuned for training duration
- EMA not applied to all parameters
The fix and how to prevent it
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