From the Wikipedia page on one of the strongest ever[1]: "Like Leela Zero and AlphaGo Zero, Leela Chess Zero starts with no intrinsic chess-specific knowledge other than the basic rules of the game. Leela Chess Zero then learns how to play chess by reinforcement learning from repeated self-play"
Leela, by contrast, requires a specialized structure of iterative tree searching to generate move recommendations: https://lczero.org/dev/wiki/technical-explanation-of-leela-c...
Which is not to diminish the work of the Leela team at all! But I find it fascinating that an unmodified GPT architecture can build up internal neural representations that correspond closely to board states, despite not having been designed for that task. As they say, attention may indeed be all you need.
More likely, the 16 million games just has most of the piece move combinations. It does not know a knight moves in an L. It knows from each square where a knight can move based on 16 million games.
The representation of the ruleset may not be the optimal Kolmogorov complexity - but for an experienced human player who can glance at a board and know what is and isn’t legal, who is to say that their mental representation of the rules is optimizing for Kolmogorov complexity either?