What FSRS v6 Is and How Polidict Uses It for More Effective Vocabulary Learning
Last updated: March 6, 2026
If a word already feels familiar, but a few days later you still cannot recall it, the problem usually is not motivation and not a "bad memory". Most often, the problem is timing. You either review too early, when it is no longer efficient, or too late, when you effectively have to learn the word again.
That is exactly the problem spaced repetition algorithms try to solve. And today, FSRS v6 is one of the most interesting and strongest implementations of that idea. At Polidict, we use FSRS not as a marketing label, but as the foundation of the actual review schedule. But with an important twist: we adapted it for practical vocabulary learning, where it is not enough to simply "know a word". You also need to recognize it, spell it, hear it, say it, and understand it in a specific meaning.
In this article, we will break down what FSRS v6 is, why people talk about it so much, and how it works inside Polidict.
If you want a shorter foundation first, start with our article about spaced repetition. And if you want the broader context of how review, active recall, and different exercise types work together, also see our article on vocabulary mastery.
What FSRS v6 means in simple terms
FSRS stands for Free Spaced Repetition Scheduler. It is an algorithm that tries to answer the main question behind any learning app: when exactly should a word appear again so that you do not forget it, but also do not waste time on unnecessary reviews.
Unlike simple schedules such as "1 day, 3 days, 7 days, 14 days", FSRS does not assign the same review plan to everything. It estimates the state of memory for a specific item and uses that to decide when the next review will be most useful.
In simplified terms, the algorithm works with three ideas:
Difficulty: how hard this material is for you personally.
Stability: how long the knowledge is likely to stay in memory.
Retrievability: how likely you are to recall the word right now.
The more stable a word is in your memory, the further the next review can be pushed. If the word starts to weaken, the algorithm brings it back earlier.
What is new in FSRS v6
FSRS does not exist in just one version, and v6 matters because it updated the forgetting-curve model and the logic for handling same-day reviews. Put less mathematically and more practically, FSRS v6 became better at two things:
Estimating how knowledge fades over time more accurately.
Handling short repeat attempts after a mistake or quick recovery more carefully.
For the learner, this means one simple thing: the schedule becomes less crude and closer to how memory actually behaves. Not all words are forgotten at the same pace. Not all mistakes mean total failure. And not every correct answer reflects the same level of mastery.
That is why FSRS v6 works well not only for classic flashcards, but also for more complex learning experiences where there are different exercise types and multiple ways to test the same word.
Why this matters especially for vocabulary
A word is not a one-dimensional object. You may recognize it perfectly when you hear it, yet still fail to spell it correctly. You may know one meaning, but get confused by another. You may read the word confidently, then freeze when you need to say it out loud quickly.
Many review systems behave as if a word is either "learned" or "not learned". For real vocabulary acquisition, that is not enough.
That is why in Polidict we use FSRS in a way that is built around a specific skill, not around an abstract card.
How Polidict uses FSRS in practice
Here is the most important part: in Polidict, there is no single shared score for "knowing a word". Instead, we keep separate FSRS states for different learning modes:
writing: whether you can produce the word yourself;
listening: whether you can recognize the word by ear;
speaking: whether you can retrieve and say it in speech;
meanings: whether you can reliably retain the meaning of the word.
On top of that, for meanings we store progress at the level of individual definitions. This matters for polysemous words. If you know the main meaning well but are shaky on a figurative or specialized one, Polidict does not collapse everything into one artificial average. The algorithm can see exactly where the weakness is.
1. Separate tracks instead of one card
This is one of the main differences between Polidict and most spaced repetition apps. We do not assume that a correct flashcard answer automatically means you are ready for writing or speaking.
The practical result is simple:
a word can be stable in listening, but unstable in writing;
one definition may need review even if another has already been mastered;
mixed training can surface exactly the aspect of the word that is currently weakest.
This makes review not just personalized, but skill-specific: the system knows not only what is weak, but how it is weak.
2. No buttons like “Again”, “Hard”, “Good”, or “Easy”
Another important Polidict choice is that we do not push the scheduling work onto the learner. In classic FSRS- or Anki-style interfaces, people often need to rate each review manually using buttons like "Again", "Hard", "Good", and "Easy".
For many people, that is inconvenient for two reasons:
you have to micro-calibrate your memory manually every time;
users often apply those ratings inconsistently.
In Polidict, we remove that layer of interface complexity. Instead of manual grading, the system looks at the actual exercise result: was the answer correct or not? At the FSRS level, that becomes a very practical rule:
a correct result is treated as successful recall;
an incorrect result is treated as a recall failure.
So the learner simply trains instead of manually operating the scheduler.
3. The first attempt matters
There is another detail that makes the schedule more honest. If you fail on the first attempt and then recover within the same session, Polidict does not pretend that this was a fully confident answer.
From a learning perspective, that is the right call: if the answer did not come out of memory immediately, the knowledge is still unstable. That is why the word may return sooner, even if you eventually got it right in the same session.
For the learner, this does not feel like punishment. It is a useful signal: you need one more good review to make the knowledge reliable.
4. In mixed training, the weakest track gets priority
When a word can appear in multiple exercise types, Polidict does not choose them randomly. In mixed training, the system checks which track is currently the most urgent: what is already overdue, what has lower stability, what is most likely to break first.
In other words, if your listening is doing relatively well but your writing for that word is weak, the system tries not to spread attention evenly. It focuses on the bottleneck.
That is one reason learning in Polidict feels more intentional. You are not just getting a random mix of exercises. You are getting the exercise that is most useful right now.
5. Writing is not pushed too early
In real learning, productive skills should often not be forced too early. If a learner still has an unstable grip on the meaning of a word, demanding confident spelling may be premature.
That is why in mixed mode Polidict does not try to prioritize writing too early if the underlying meaning work is still not stable enough. In plain terms: the system first helps you understand the word confidently, and only then pushes it harder into productive use.
This is a small detail in the training logic, but details like this often separate a system that feels good to use every day from one that keeps creating unnecessary friction.
What this gives Polidict users
All of this sounds technical, but the actual benefit is very practical.
You do less unnecessary review
If a word is already stable in a given track, Polidict does not keep pushing it in front of you without reason. That reduces fatigue and keeps learning from turning into an endless conveyor belt of familiar cards.
You see weak points instead of an illusion of progress
One of the common traps in language learning is thinking a word is already "done" just because you recognized it somewhere. Separate FSRS tracks in Polidict help avoid that illusion. You can clearly see that a word may already be familiar by ear, but still not ready for active writing.
You spend less energy managing the algorithm
You do not need to think about which button to press, whether something felt "Hard" or "Good", whether you were too generous with yourself, or too strict. Polidict removes that micromanagement. You just do the exercise, and the system handles the planning.
The schedule matches real word use better
In language learning, what matters is not abstract memory of a card, but the ability to retrieve a word in the right format at the right moment. That is why separate tracks for writing, listening, speaking, and meanings produce a more realistic learning outcome than one averaged score for the whole word.
Who this approach is best for
FSRS v6 in Polidict is especially useful if you:
study a language seriously and want to build a working vocabulary, not just run through cards;
work with polysemous words, technical terms, phrases, or specialized vocabulary;
want to combine receptive knowledge with productive use;
dislike complex interfaces and do not want to manage difficulty buttons after every answer.
In short, this approach is for people who care about review quality, not just review quantity.
Common questions
Why doesn’t Polidict have buttons like “Easy” or “Hard”?
Because we do not want the user to act as the scheduler operator. In most cases, the real exercise result already gives enough information: you either retrieved the answer from memory or you did not.
Why can a word return in writing training if I already know it from a flashcard?
Because those are different skills. Recognizing a word and producing it yourself are not the same thing. In Polidict, they are scheduled separately.
Why can a word come back again if I just corrected a mistake?
Because the first attempt matters. If the correct answer only appeared after an error, the system sees the knowledge as still unstable and schedules extra reinforcement.
Does this mean FSRS fully automates learning?
No. The algorithm is good at answering when to review, but it does not decide what you should learn or in what context you should use a word. That is why in Polidict, review is only one part of the picture alongside good definitions, examples, audio, and different training types.
Conclusion
FSRS v6 is not just another new acronym from the world of learning apps. It is a strong modern approach to review scheduling that reflects real memory behavior better than fixed or overly rough intervals.
At Polidict, we use it in a way that serves actual vocabulary learning:
we track writing, listening, speaking, and meanings separately;
we schedule reviews without manual difficulty buttons;
we take into account whether the answer was correct on the first attempt;
we prioritize the aspect of a word that genuinely needs attention right now.
The result is what serious language learners usually want: less randomness, less interface noise, fewer unnecessary reviews, and more sense that the system is actually helping you move forward.
If you want to go deeper next, the most logical follow-up reads are:
Spaced Repetition for Vocabulary for a foundational explanation of the approach and its role in Polidict.
Vocabulary Mastery: Strategies and Polidict for a broader look at how spaced repetition works together with practice, context, and long-term retention.
If you want to see how this works in practice, try Polidict and let the algorithm do the most important thing: bring a word back exactly when it makes sense.