Imagine you collected a billion unique, feasible board positions (all positions is intractable, but most possible positions are impractical) and the best nest m

Imagine you collected a billion unique, feasible board positions (all positions is intractable, but most possible positions are impractical) and the best nest move for each. That "best next move" is the result of a tree search. Now use a transformer to "compress" that information into its model. It sounds like that is approximately what is going on here. Certainly, the model is likely to generalize some aspects of the data (just like LLMs do). But for the most part, the model encodes the information from the Stockfish evaluation. (This is just my guess of what we are seeing.)