FSRS vs SM-2 for Chess: Why the Modern Algorithm Wins for Opening Retention

Disclosure: ChessAtlas is our product, and we use FSRS as our default scheduler. We've aimed for a fair, source-backed comparison, but readers should weigh our perspective accordingly.

You memorize the Ruy Lopez Closed to move 12. You drill it for a month. On game day, your opponent plays 3...a6 4.Ba4 Nf6 5.O-O Nxe4 (the Open Variation) and the position you thought you knew cold slips away. This is not a motivation problem, it is a scheduling problem. The algorithm deciding when to show you a position is the difference between lines that stick and lines that vanish under time pressure.

Most chess opening trainers still run on SM-2, the spaced repetition algorithm Piotr Woźniak published in 1987 for SuperMemo. A newer algorithm, FSRS (Free Spaced Repetition Scheduler), was released publicly for Anki in December 2022 and became the default in Anki 23.10 (November 2023). The shift matters because FSRS personalizes intervals to your recall history and, per the official benchmark and Chessbook's own migration announcement, cuts review load by roughly 20 to 30 percent at the same retention level.

If you already build a repertoire with the framework covered in our pillar guide, the algorithm under the hood is what decides whether your study hours compound or leak.

What Are FSRS and SM-2?

Both algorithms schedule reviews to maximize long-term memory while minimizing wasted effort. In chess, they decide whether you see a critical Najdorf branch tomorrow, next week, or next month based on how accurately you played it last time.

SM-2: The 1987 Heuristic Still Running Most Chess Trainers

SM-2 uses fixed multipliers and an "ease factor" that moves up or down based on your Hard / Good / Easy rating. It treats every user identically, which was the only option given the compute and data available in 1987. The common failure mode, widely documented in the Anki community, is ease hell: rate a card Hard too many times and the ease factor collapses, forcing the card to return daily long after you have actually mastered it. In chess, this happens regularly with tight move orders (Winawer French, Mar del Plata King's Indian, Sveshnikov Sicilian) where you fumble early, rate Hard, and then never escape the short interval.

SM-2 powers Chessable's MoveTrainer, Anki's legacy scheduler, and Chess Position Trainer's custom variant. It works, it is simply not tuned to what 40 years of spaced-repetition data have since revealed.

FSRS: Personalized Scheduling Trained on Real Anki Reviews

FSRS was built by Jarrett Ye (MaiMemo Inc.) and released as an Anki add-on in December 2022. Its default parameters are fit on hundreds of millions of reviews from roughly 10,000 Anki users, then adapt to your personal review history. It models memory with three variables:

• Retrievability (R): probability you recall a position right now
• Stability (S): days required for retrievability to drop from 100% to 90%
• Difficulty (D): how hard this specific position is for you

You also set an explicit target retention (often 85 to 95 percent). FSRS schedules intervals to meet that target on a per-card basis. The Expertium benchmark, the public leaderboard comparing spaced-repetition algorithms on real review data, consistently puts FSRS ahead of SM-2 by the 20 to 30% review-reduction margin.

Why FSRS Matters Specifically for Chess Opening Retention

Chess openings form deeply interdependent trees. A single choice on move three shapes the playable options five moves later, and one memory lapse collapses the line. A serious club-level repertoire typically runs to a few thousand position cards once transpositions and opponent sidelines are included. The following four properties of FSRS map directly onto these demands.

1. Personalization Between Forced and Strategic Positions

Some positions stabilize fast in your memory once understood, because the only correct continuation is forced. Others remain fragile for weeks because several plausible-looking moves exist and only one is best. FSRS learns which is which for you; SM-2 applies the same multipliers to both.

By contrast, a pure tactical refutation, like punishing an early ...Qh4 in the Scotch with 5.Nc3, tends to stabilize within a handful of correct recalls. FSRS widens the interval on that card quickly, freeing review time you would otherwise waste.

2. Escape from Ease Hell on Sharp Lines

With SM-2, repeatedly rating a complex Grünfeld or Mar del Plata line as Hard drops the ease factor, forcing daily reviews long after you have internalized the position. FSRS recalibrates stability every time you recall correctly, so once the line clicks, the interval stretches out. Reviews follow your actual learning curve rather than punishing you for the weeks you struggled.

3. Configurable Retention for Tournament Prep

Before a weekend open, you may want 95% retention on your main lines and accept 85% on sidelines. FSRS lets you set an explicit retention target, per deck or globally, and adjusts intervals to hit that goal. SM-2 has no equivalent dial.

4. Transpositions Handled as Shared Positions

Two different move orders can reach the same tabiya. FSRS-driven trainers that store by position rather than by sequence recognize this and schedule one card rather than two. This is an implementation detail more than an algorithm property, but it pairs naturally with FSRS-based tools like ChessAtlas and keeps your deck size grounded in real positions.

How FSRS Works Under the Hood

After each review, FSRS updates stability (S), difficulty (D), and retrievability (R) for that specific card. The next interval is computed so that R drops to your target retention at exactly that time. The key difference from SM-2 is that the entire review history informs the next interval, not just the last rating.

Default FSRS parameters come from the large-scale Anki dataset, so the algorithm performs reasonably from day one. With more of your own reviews, the optimizer can be rerun to fit parameters to your specific forgetting curve. On Anki this happens in the deck settings; on ChessAtlas it is automatic.

What Changed When Chess Trainers Switched to FSRS

Chessbook: Official 20 to 30% Reduction Claim

Chessbook migrated its scheduler to FSRS 4.5 in June 2024 and reports the same 20 to 30 percent review reduction at equal retention. Users building deep Sicilian or Najdorf repertoires see volatile theory reinforced more often while mastered lines are spaced out, without touching the parameters manually.

Anki: FSRS Is Now the Default Scheduler

Since Anki 23.10, FSRS is the default for new installs. The Anki community's most upvoted migration reports converge on the same pattern: fewer daily cards for the same long-term retention, especially on large decks. For chess players running 2,000 to 5,000 position Anki decks, the practical effect is shorter daily sessions at the same or better retention.

ChessAtlas: FSRS from Day One

ChessAtlas ships with FSRS as the default scheduler, and pairs it with automatic Lichess and Chess.com game import plus Deviation Finder. When Deviation Finder flags a position you left your prep in a real game, that card is pushed to the review queue with FSRS scheduling. The feedback loop between study and real games stays tight.

For how these tools stack up in detail, see our 2026 trainer comparison.

Quick Comparison: FSRS vs SM-2 at a Glance

Common Misconceptions

"FSRS only helps if I have thousands of cards"

Default FSRS parameters already improve scheduling on small decks thanks to the global training data. Your personal fit sharpens over time, but configurable retention and dynamic intervals reduce waste from the first review. If your repertoire has 500 to 1,000 positions, the configurable retention dial alone is worth the switch.

"SM-2 is obsolete"

SM-2 still works. Chessable has shipped world-class courses on SM-2 for years and players legitimately memorize openings with it. The argument is not that SM-2 is broken, it is that FSRS is measurably more efficient and the switching cost is low. If a tool you love uses SM-2, stay; if you are picking a new trainer, pick FSRS.

"Switching scheduler resets my progress"

Migrations in Anki and Chessbook preserve review history. FSRS seeds initial parameters from your past ratings, then calibrates over a few weeks of reviews. You keep intervals and accuracy signals rather than starting from scratch.

Which Tools Use Which?

• FSRS by default: ChessAtlas, Chessbook (since 2024), Anki 23.10+
• FSRS opt-in: Older Anki decks (enable in deck options), community tools like Chessdriller
• SM-2 still: Chessable MoveTrainer, Chess Position Trainer (legacy custom scheduler close to SM-2)
• No spaced repetition at all: ChessBase (database tool), Lichess Opening Explorer, most engine analysis tools

For the broader repertoire-tools landscape, see our 7 tools roundup and the Chessable alternatives guide.

Your Micro-Action Today

• If you use Anki or Chessbook, switch the scheduler to FSRS if you have not already. Set retention to 90%.
• If you use Chessable, consider exporting your repertoire PGN and drilling it in a FSRS-based tool like ChessAtlas in parallel.
• Give it two weeks. FSRS needs that long to calibrate from your review history.
• Measure: daily review count and subjective recall under time pressure. The 20 to 30% reduction shows up within a month.

For the broader study framework this algorithm plugs into, read How to Build a Chess Opening Repertoire That Actually Sticks, why spaced repetition works for chess, and how to memorize chess openings and actually remember them. Or create a free ChessAtlas account and start drilling with FSRS in two minutes.