# What is Exploding Gradient Problem? Definition & Technical Mechanics

> **Source:** [SPIDITS AI AI Glossary](https://spidits.com/ai-glossary)  
> **Category:** Model Training  
> **Canonical Citation:** [https://spidits.com/ai-glossary/exploding-gradient-problem](https://spidits.com/ai-glossary/exploding-gradient-problem)

## Definition
The Exploding Gradient Problem is an error during backpropagation training where gradients accumulate, resulting in unstable, massive parameter updates that prevent model weights from converging.

## Architectural Mechanics & Deep Dive
The exploding gradient problem is a neural network training instability where gradients accumulate and grow exponentially during backpropagation. This causes extremely large updates to network weights, making training unstable and causing the loss to overflow to NaN (Not a Number). It is common in deep recurrent networks, and is managed using gradient clipping and residual connections.

## Industry Applications & Real-World Use Cases
Neural network debugging, optimizer setup, and deep network configuration.

## Frequently Asked Questions

### How do you prevent exploding gradients?
By using gradient clipping (capping gradient value magnitudes) or employing weight initialization strategies like Xavier or He initialization.

### What are the symptoms of exploding gradients?
Loss function values displaying `NaN` during training logs, or weights rapidly expanding to infinity.

## Related Terminology
- [backpropagation](https://spidits.com/ai-glossary/backpropagation)
- [loss-function](https://spidits.com/ai-glossary/loss-function)
- [vanishing-gradient-problem](https://spidits.com/ai-glossary/vanishing-gradient-problem)

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*From the SPIDITS AI Knowledge Base (336+ terms indexed). Explore full technical definitions: [https://spidits.com/ai-glossary](https://spidits.com/ai-glossary)*