What Is Curriculum Learning? Training on Easy Examples Before Hard Ones
Borrowing an idea from how humans learn, curriculum learning deliberately orders training examples from easy to hard, rather than presenting them in random order.
Ordering training examples deliberately
Curriculum learning deliberately orders the examples a model trains on, typically from easier to progressively harder, rather than presenting the entire training dataset in a random, unordered sequence — borrowing the intuition behind how a school curriculum builds up from basics to more advanced material.
Why ordering can genuinely help
Starting with simpler examples can help a model establish basic, correct patterns before being confronted with harder, noisier, or more ambiguous ones — similar in spirit to how a person generally learns arithmetic before calculus rather than encountering both simultaneously in random order.
Why it isn't used universally
Defining what counts as "easy" versus "hard" for a given task and dataset isn't always obvious, and standard random-order training already works quite well for large language models trained on huge, general text corpora — curriculum learning shows more consistent benefit for certain specialized tasks or smaller-scale training than for large-scale general pretraining.
Frequently Asked Questions
What is curriculum learning?
A training approach that deliberately orders training examples from easier to progressively harder, rather than presenting the entire dataset in a random, unordered sequence.
Is curriculum learning used for training large language models?
It's more common for certain specialized tasks or smaller-scale training — standard random-order training already works well for large-scale general pretraining, partly because defining "easy" versus "hard" isn't always obvious at that scale.