This is my one doubt about this new algorithm. The work @L.M.Sherlock is doing is fantastic and I’m super appreciative, but I do have a concern about how well this will adapt outside of language learning. I’m hoping the optimizer takes care of that.
To be specific I’ve always taken the Anki algorithm/SM-2 to be inefficient for my use case for SRS, i.e. it results in too many reviews and gives me an excessive workload. So I was surprised to see FSRS giving even short intervals in many cases, resulting in more reviews. The SM-18 algorithm by contrast generally results in much longer intervals and thus fewer reviews.
I would assume that’s due to large differences in SRS use between different people, and the datasets used to develop the algorithms. From what I understand Wozniak used much smaller datasets that were basically hand picked - his own, and other experienced users of SueprMemo. By the nature of SuperMemo, and the emphasis in that community, people are generally not using it for rote memorization of asemantic information, but rather for retaining things that have already been understood, learned, are building upon current existing knowledge in a semantic way. Along with an emphasis on high quality card formulation.
By contrast the average SRS in most apps is not as skilled, and the most common use case is rote memorization of the new vocabulary in a foreign language - something that is not very semantic until 1) you’re getting quite good in the language, 2) you get good at card formulation for language learning. So again, if we’re taking huge datasets, the average person just isn’t going to be that great. Using SRS well is a skill developed over time and with effort.
Of course this difference in approach probably leads to SM-18 pretty bad for retention for a brand new SRS user try to rote memorize vocab in a new language, and SM-2/FSRS being pretty inefficient in terms of excess reviews for a highly skilled SRS user trying to retain semantic knowledge. The advantage of SM-2/FSRS I guess is that they cater for more users, in that they still fulfil their intended purpose of retention, even if they are inefficient for certain users, whereas SM-18 simply won’t provide the intended retention for an unskilled user. That does provide a forcing function though, and is why those that stick with SuperMemo tend to become highly skilled SRS users.
Both SM-18 and FSRS adapt to the user though, so the big question in my mind is how quickly and how well do they adapt for a user for which the initial algorithm is far from optimal.