Starting with simpler examples accelerates learning
Curriculum learning orders the training data from easy to hard, mimicking how humans learn. A student learning to parse English might start with simple declarative sentences, then progress to relative clauses, then to garden-path sentences that trick initial parsing. The model builds intermediate representations that are useful for harder examples.
This ordering prevents early stalling on hard examples. If the first batch is entirely hard, the model may get stuck in a bad basin of the loss landscape; if it starts easy, it reaches a region where hard examples are easier to tackle.
Measurable convergence improvements
On synthetic tasks like learning to count or sort, curriculum learning can reduce training time by 50% or more compared to random order. The benefit is largest when examples vary widely in difficulty and the model can leverage early progress on simple cases.
In language model pretraining, the benefit is smaller but still measurable: training on clean, frequent data first, then rare/noisy data later often converges faster than random shuffling.
Designing an effective curriculum
The curriculum itself is a hyperparameter. Too steep a gradient (easy to hard very quickly) and you lose the benefit; too shallow and you waste time on trivial examples. Some systems estimate difficulty on the fly, dynamically increasing the fraction of hard examples as the model improves.