# The right time to learn: mechanisms and optimization of spaced learning

arXiv: https://arxiv.org/abs/1606.08370 · HTML: https://mlsanity.com/paper/1606.08370/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2016.

## Built on by (most cited first, 5 of 5)

- [Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models](https://mlsanity.com/paper/2205.10770/) (2022)
- [Learn the Time to Learn: Replay Scheduling in Continual Learning](https://mlsanity.com/paper/2209.08660/) (2022)
- [Accelerating Large Language Model Pretraining via LFR Pedagogy: Learn, Focus, and Review](https://mlsanity.com/paper/2409.06131/) (2024)
- [Siamese Network with Dual Attention for EEG-Driven Social Learning: Bridging the Human-Robot Gap in Long-Tail Autonomous Driving](https://mlsanity.com/paper/2504.10296/) (2025)
- [Right Time to Learn:Promoting Generalization via Bio-inspired Spacing Effect in Knowledge Distillation](https://mlsanity.com/paper/2502.06192/) (2025)

## Abstract (arXiv)

For many types of learning, spaced training that involves repeated long inter-trial intervals (ITIs) leads to more robust memory formation than does massed training that involves short or no intervals. Several cognitive theories have been proposed to explain this superiority, but only recently has data begun to delineate the underlying cellular and molecular mechanisms of spaced training. We review these theories and data here. Computational models of the implicated signaling cascades have predicted that spaced training with irregular ITIs can enhance learning. This strategy of using models to predict optimal spaced training protocols, combined with pharmacotherapy, suggests novel ways to rescue impaired synaptic plasticity and learning.