Spaced repetition algorithms are a minefield. I have developed and partially validated an unpublished (so far) SR algo. The biggest failure of existing SR algorithms and something which has been studied in literature since 2016 a phenomemon in the human brain where retrieval performance of an item you want to learn becomes easier and harder seemingly at random. It turns out that there are three distinct phases of memory and your brain switches between them over time as it consolidates things you learn. A regression of performance over time becomes complicated. There are ways to deal with this using regime switching markov decision processes, but it is incredibly easy to get it all wrong. Another essential component of spaced repetitions so many algorithms get wrong is ignoring the spacing effect.

We all have something we can improve with practice and effort

Spaced repetition approaches model ngram learning rate and forgetting rate over time. The multi armed bandit method does not make any assumptions about performance over time. Rather, it is an attempt to discover your current abilities. The dynamic component of thompson sampling is implemented to ensure recent changes in per-ngram performance are weighted more heavily than older data points in the underlying statistical model. Thus what is being modeled is a best effort estimate of your abilities given limited exploration time.