Gradient Explosion
Gradient explosion causes loss spikes, NaN loss, and training instability, especially in RNNs and deep networks.
Gradient explosion causes loss spikes, NaN loss, and training instability, especially in RNNs and deep networks.
What this failure is
Gradient Explosion is a Training Stability failure seen during ML training runs. Gradient explosion causes loss spikes, NaN loss, and training instability, especially in RNNs and deep networks. Common tags: Gradient Explosion, Gradient Clipping, Training Stability, Rnn.
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Why it happens (the mechanism)
Gradient norm exceeds threshold. Recurrent weight matrices amplify gradients. No gradient clipping. Learning rate too high. Unstable loss landscape. 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 spikes occasionally
- NaN loss from large gradients
- Model parameters become unstable
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| Gradient norm grows very large | Gradient norm exceeds threshold |
| Loss spike followed by divergence | Recurrent weight matrices amplify gradients |
| Loss is inf or NaN | No gradient clipping |
Which systems are affected
- RNN/LSTM training
- Deep network training
- Training without normalization
- GAN discriminator training
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: Gradient norm grows very large
- ✓Verified signal present: Loss spike followed by divergence
- ✓Verified signal present: Loss is inf or NaN
- ✓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
- Gradient norm exceeds threshold
- Recurrent weight matrices amplify gradients
- No gradient clipping
- Learning rate too high
- Unstable loss landscape
The fix and how to prevent it
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