Curriculum Learning
A training strategy inspired by human education where the model is exposed to training examples in a meaningful order — starting with easier examples and gradually increasing difficulty.
Why It Matters
Curriculum learning can speed up training and improve final model performance, especially for complex tasks where diving into hard examples immediately causes learning instability.
Example
Training a language model first on simple, well-structured sentences, then gradually introducing complex, noisy text — mirroring how children learn language.
Think of it like...
Like a math curriculum that starts with addition, then multiplication, then algebra, then calculus — building knowledge progressively rather than throwing everything at the student at once.
Related Terms
Meta-Learning
An approach where models 'learn to learn' — they are trained across many tasks so they can quickly adapt to new tasks with minimal data. Also called learning to learn.
Transfer Learning
A technique where a model trained on one task is repurposed as the starting point for a model on a different but related task. Instead of training from scratch, you leverage knowledge the model has already acquired.