Training & Fine-tuning

Learning Rate

How quickly a model updates its weights during training — a critical training hyperparameter.

Definition

The learning rate controls how large a step the model takes each time it updates its parameters during training. Too high, and the model overshoots improvements and training becomes unstable. Too low, and training takes impractically long. Finding the right learning rate — and often varying it across training — is one of the key decisions in training AI models. It is a hyperparameter, meaning it is set by the trainer before training begins, not learned from data.

Related Terms

Gradient Descent

The algorithm models use to gradually improve during training by reducing errors.

Backpropagation

The process of passing error signals backwards through a network to update weights.

Fine-tuning

Further training a pre-trained model on specific data to specialise it for a task.

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Disclaimer

This definition is provided for educational and informational purposes only. It represents a general explanation of a technical concept and does not constitute professional, technical, or investment advice. Artificial intelligence is a rapidly evolving field; terminology, techniques, and capabilities change frequently. Coaley Peak Ltd makes no warranty as to the accuracy, completeness, or currency of the information provided. Nothing on this page should be relied upon as the sole basis for commercial, technical, legal, or investment decisions without independent professional advice.

Document reference: ISO_webpage_knowledge-base_glossary_v1

Last modified: 29 March 2026

Knowledge Base·Training & Fine-tuning·Learning Rate