u/masterchiefcodes

Hey all — a few weeks ago I posted about my paper being accepted to the IEEE Conference on Games 2026 on using preference learning to improve fine-tuning for Maia-style chess models.

Since then I’ve been building out the ideas further in a website called garrychess.ai.

Last night I released a feature that lets anyone:

• import their Chess.com or Lichess games
• train a personalized chess bot from their own games
• play against it in-browser
• share the bot with friends via a public link

The system tries to preserve stylistic tendencies rather than simply maximize engine strength.

I’ve also been experimenting with style embeddings trained on a few million games to estimate similarity between players and various grandmasters. The same embeddings are currently being used to cluster puzzles by style/archetype (e.g. positional/Karpov-like middlegames vs sharper tactical positions).

Some observations so far:

• openings are surprisingly reproducible from relatively small datasets
• it picks up a mixed style if multiple people play on the account. My girlfriend's style clusters were nakamura (counter attacker) for middle game (when I sometimes start playing) and fischer endgame.
• My bot seemed to capture my stronger intensity in the lategame and blunder propensity midgame.

Training currently costs $1 per bot to roughly cover GPU/training costs. Playing against generated bots and the style-based puzzle features are free.

If anyone here tries it, I’d love feedback on:

• realism of the generated bots
• evaluation ideas for stylistic fidelity
• better approaches for human-like search/sampling
• whether the style clustering actually feels meaningful

The paper the models are based on was accepted recently to IEEE Conference on Games (oral / speaker presentation).

You can play against models trained on:

1. Garry Kasparov
2. Anatoly Karpov
3. Bobby Fischer
4. Magnus Carlsen
5. Judit Polgar
6. More soon

The models can be tuned to be anywhere from 2100-2800 approximate ELO and are much more realistic than the Stockfish +hardcoded chess.com bots (hopefully!)

For more info check out:

Demo - https://garrychess.ai

PDF (first draft submission, not camera ready) - https://drive.google.com/file/d/1qiqwGH57pe-lHIzwa79Qaww6M-WVUvy2/view

Discord community for project updates - https://discord.gg/ANNZ78c7

The training process uses preference optimization to get data about a given player using not just what move they picked, but also moves they rejected that were strong candidates. This makes it learn more quickly and effectively than the usual fine-tuning process.