FSRS models memory with a power-law forgetting curve, fit on 1.7 billion real flashcard reviews. Here is how fast recall fades without review — and how spaced repetition stretches the curve by building memory stability.
| Days since last review | Newly learned card (1-day stability) | Reinforced by spaced repetition (30-day stability) |
|---|---|---|
| 1 | 0.9 | 0.996 |
| 3 | 0.766 | 0.988 |
| 7 | 0.615 | 0.974 |
| 14 | 0.483 | 0.949 |
| 30 | 0.353 | 0.9 |
| 60 | 0.258 | 0.825 |
| 90 | 0.213 | 0.766 |
Every card you learn starts fading the moment you stop reviewing it. Hermann Ebbinghaus first mapped this forgetting curve in 1885. Modern spaced-repetition research has since measured it at massive scale — and found that memory decays as a power law, not the smooth exponential curve from the textbooks.
Quizlar schedules reviews with FSRS (the Free Spaced Repetition Scheduler), the open-source algorithm now built into Anki. FSRS models the probability you still recall a card after t days as:
R(t) = (1 + (19/81) · t / S)−0.5
Here S is the card's memory stability — the number of days until recall is expected to fall to 90%. The curve above is plotted directly from this formula using FSRS's default, benchmark-fit parameters.
The two lines tell the whole story of spaced repetition. A freshly-learned card (stability ≈ 1 day) decays fast — predicted recall drops to about 62% within a week and 35% within a month. The same card, reinforced by spaced reviews until its stability reaches ~30 days, still holds ~90% recall at a month and ~77% at 90 days.
Crucially, the shape of the curve never changes. Spaced repetition doesn't make you forget more slowly per card — it raises stability, stretching the same curve out over weeks and months. Each successful review compounds stability, which is why a few minutes a day beats one long cram before an exam.
The classic Ebbinghaus curve is often drawn as exponential decay. Large-scale review data shows real forgetting has a heavier tail: you forget quickly at first, then forgetting slows. FSRS's power-law form captures this, which is part of why it predicts recall more accurately than older schedulers like SM-2.
The forgetting-curve parameters used here come from the open-spaced-repetition benchmark — an open dataset of roughly 1.7 billion flashcard reviews from about 20,000 Anki users, the largest public spaced-repetition dataset. The curve is the FSRS model fit on that data, not a measurement of Quizlar's own users. Because Quizlar runs the same FSRS engine, it is also exactly how Quizlar decides when to bring each of your cards back.
Large-scale review data shows forgetting follows a power law, not a simple exponential. FSRS uses the power function R(t) = (1 + (19/81)·t/S)^−0.5, which fits real recall data more accurately than the classic exponential Ebbinghaus curve.
Stability is the number of days until your predicted recall of a card drops to 90%. Every successful review increases stability, so the card can wait longer before its next review.
No. These curve parameters come from the open-spaced-repetition benchmark — about 1.7 billion reviews from ~20,000 Anki users. Quizlar uses the same FSRS algorithm, so the curve also describes how Quizlar schedules your reviews.
It doesn't change the curve's shape — it raises stability. By reviewing each card right before you would forget it, FSRS stretches the same forgetting curve across longer and longer intervals.