AdamW Weight Decay Misconfiguration
AdamW weight decay misconfiguration causes poor generalization or unstable training.
AdamW weight decay misconfiguration causes poor generalization or unstable training.
What this failure is
AdamW Weight Decay Misconfiguration is a Training Stability failure seen during ML training runs. AdamW weight decay misconfiguration causes poor generalization or unstable training. Common tags: Adamw, Weight Decay, Optimizer, Regularization.
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Why it happens (the mechanism)
Weight decay applied to all parameters including biases and norms. Weight decay too high or too low. Weight decay not applied to correct parameter groups. Learning rate and weight decay not balanced. 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 doesn't improve despite good setup
- Model overfits or underfits
- Validation metrics plateau
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| Loss: nan, val_loss: high | Weight decay applied to all parameters including biases and norms |
| Loss decreases but val_loss increases | Weight decay too high or too low |
| Model doesn't converge | Weight decay not applied to correct parameter groups |
Which systems are affected
- Fine-tuning with AdamW
- Training from scratch with AdamW
- Transfer learning with weight decay
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: nan, val_loss: high
- ✓Verified signal present: Loss decreases but val_loss increases
- ✓Verified signal present: Model doesn't converge
- ✓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
- Weight decay applied to all parameters including biases and norms
- Weight decay too high or too low
- Weight decay not applied to correct parameter groups
- Learning rate and weight decay not balanced
The fix and how to prevent it
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