The unstable gradient problem is a fundamental problem that occurs in a neural network, that entails that a gradient in a deep neural network tends to either explode or vanish in early layers.

The unstable gradient problem is not necessarily the vanishing gradient problem or the exploding gradient problem, but is rather due to the fact that gradient in early layers is the product of terms from all proceeding layers. More layers make the network an intrinsically unstable solution. Balancing all products of terms is the only way each layer in a neural network can close at the same speed and avoid vanishing or exploding gradients. Balanced product of terms occurring by chance becomes more and more unlikely with more layers. Neural networks therefor have layers that learn at different speeds, without being given any mechanisms or underlying reason for balancing learning speeds.

When magnitudes of gradients accumulate, unstable networks are more likely to occur, which is a cause of poor prediction results.

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