LLMs learn faster if we first pretrain them to imitate dense teacher-forced examples6. I speculated that this would work on humans too, so I built a chess app7 where you try to imitate Stockfish. My theory is that this will help humans quickly become OK at chess, but they will reach a wall where practice on full games is more efficient than continued pretraining1. I also think the app is fun.

This is probably not an efficient way to learn the basic rules of chess8, and you’ll need to train openings9 separately. The idea of chess puzzle apps is hardly unique, but I don’t think anything else works in exactly the same way2.

The app's preview card: a chess position with two candidate moves drawn as arrows numbered 1 and 2, beside matching numbered buttons labeled Bxh7+ and Ne5 under the question "Which move is better?" Tagline: "Real positions, two candidate moves, instant feedback. Difficulty adapts to hold you near 80% accuracy."

https://chess-pretraining.brendanlong.com7

How does the game work?

We show you a board state from a real game, and two moves (generally the real move vs. what Stockfish would play). You pick which move you think is better, and then we tell you what Stockfish thinks, and show you how Stockfish would play out the next few moves.

There are also options to see the other move, to copy the info (to ask an AI to explain it), to share a link to the same problem with a friend, and an Elo-style3 number so you can watch the number go up4.

How do we pick moves?

We collect a bunch of games from Lichess10 (filtered to exclude certain fast game types), pick positions where the move mattered and the game wasn’t already over, and then run the moves through Stockfish.

If the real player and Stockfish disagree, we save the real move, Stockfish’s move, Stockfish’s win percentage for each move, and what Stockfish would play after each move. In the rare case where players agree with Stockfish, we save Stockfish’s second best move as an alternative.

When someone opens the app, we try to show a move that they can discriminate with 80% accuracy5. This still needs more calibration, but the calculation is based on the win probability gap given a shallow search.

There’s a lot more detail about how the app works in the spec11.

More details

The code is open source and MIT licensed. You can find it on GitHub: brendanlong/chess-pretraining-for-humans12

In terms of privacy13, I collect the obvious data (your answers, plus account info if you sign up), and general usage data (how often do people open each page). I may run my own analyses (do people actually improve quickly?) and/or share anonymized data with other researchers.

Try it

If this sounds interesting to you, the app is live7 and you can try it instantly with no signup.

  1. If this app gets any traction, I’ll measure how quickly people improve and compare it to Lichess ratings14. ↩

  2. Chessort15 is similar to my app, but has you rank 4 moves. This is actually a better match for dense pretraining over a teacher model’s logit distribution than my app, but it’s so difficult that I’m not convinced it’s helpful. ↩

  3. The Elo number is calibrated from the Elo of players making the moves you’re judging, but it’s very unlikely to match your real chess Elo. ↩

  4. Or watch the number go down, if you’re bad at chess like me. ↩

  5. Learning seems to be fastest around 85% accuracy16. 80% makes it slightly easier for us to calibrate our ratings. ↩