and these variants don’t even remove any pieces—they’re just small tweaks like permitting self-capture or forbidding castling within the first 10 moves
You’re framing these as being closer to “regular” chess, but my intuition is the opposite. Most of the game positions that occur during a queen-odds game are rare but possible positions in a regular game; they are contained within the game tree of normal chess. I’m not sure about Stockfish in particular, but I’d expect many chess AIs incorporating machine learning would have non-zero experience with such positions (e.g. from early self-play runs when they were making lots of bad moves).
Positions permitting self-capture do not appear anywhere in that game tree and typical chess AIs are guaranteed to have exactly zero experience of them.
ETA: It also might affect your intuitions to remember that many positions Stockfish would never actually play will still show up in its tree search, requiring it to evaluate them at least accurately enough to know not to play them.
I disagree. By starting with impossible positions like a queen already being missing*, the game is already far out of the superhuman-level chess-game distribution which is defined by Stockfish. Stockfish will never blunder in the early game so badly as to lose a queen in a normal early-game position, even if it was playing God. I expect these to be positions that the Stockfish policy will never reach, not even with its weakest play of zero tree search & following deterministic argmax move choice. The only time Stockfish would ever reach such positions is if forced to by some external force like a player fiddling with settings or a strange training setup, or, like, a cosmic ray flipping some bits on the CPU. There might be some such blunders very early on in training which takes it into such imbalanced very early positions, but those are still fairly different, and the final Stockfish is going to be millions (or at this point, billions) of games of training later and will have no idea of how to handle some positions that near-random play produced eons ago and long-since washed out. (After all, those will be the very stupidest and most incompetent games it ever played, so there is little value in holding onto them in any way. Most setups will erase old games pretty quickly, and certainly don’t hold onto games from the start.)
Whereas several of the changes Kramnik evaluated, like ‘Forbidding castling within the first 10 moves’ probably overlaps to quite a considerable degree; what fraction of chess games, human expert or Stockfish, involve no castling in the first 10 moves and so accidentally fulfill that rule? Probably a pretty good chunk!
* even odds like knight-odds -where you can at least in theory construct the position during a game, by moving the knight out, capturing it with the other knight, and carefully moving the other knight back into its original position—have exactly zero probability of ever occurring in an on-policy game.
Several? I can see one (the one you cite). Some of the other variants—e.g., no castling at all, or pawns can’t move two squares on their first move—can lead to positions that also arise in normal chess. But having neither side castle at all is really unusual and most such positions will be well out of distribution; and it’s very common for some pawns to remain on the second rank all the way to the endgame, where the option of moving one or two squares can have important timing implications.
You’re framing these as being closer to “regular” chess, but my intuition is the opposite. Most of the game positions that occur during a queen-odds game are rare but possible positions in a regular game; they are contained within the game tree of normal chess. I’m not sure about Stockfish in particular, but I’d expect many chess AIs incorporating machine learning would have non-zero experience with such positions (e.g. from early self-play runs when they were making lots of bad moves).
Positions permitting self-capture do not appear anywhere in that game tree and typical chess AIs are guaranteed to have exactly zero experience of them.
ETA: It also might affect your intuitions to remember that many positions Stockfish would never actually play will still show up in its tree search, requiring it to evaluate them at least accurately enough to know not to play them.
I disagree. By starting with impossible positions like a queen already being missing*, the game is already far out of the superhuman-level chess-game distribution which is defined by Stockfish. Stockfish will never blunder in the early game so badly as to lose a queen in a normal early-game position, even if it was playing God. I expect these to be positions that the Stockfish policy will never reach, not even with its weakest play of zero tree search & following deterministic argmax move choice. The only time Stockfish would ever reach such positions is if forced to by some external force like a player fiddling with settings or a strange training setup, or, like, a cosmic ray flipping some bits on the CPU. There might be some such blunders very early on in training which takes it into such imbalanced very early positions, but those are still fairly different, and the final Stockfish is going to be millions (or at this point, billions) of games of training later and will have no idea of how to handle some positions that near-random play produced eons ago and long-since washed out. (After all, those will be the very stupidest and most incompetent games it ever played, so there is little value in holding onto them in any way. Most setups will erase old games pretty quickly, and certainly don’t hold onto games from the start.)
Whereas several of the changes Kramnik evaluated, like ‘Forbidding castling within the first 10 moves’ probably overlaps to quite a considerable degree; what fraction of chess games, human expert or Stockfish, involve no castling in the first 10 moves and so accidentally fulfill that rule? Probably a pretty good chunk!
* even odds like knight-odds -where you can at least in theory construct the position during a game, by moving the knight out, capturing it with the other knight, and carefully moving the other knight back into its original position—have exactly zero probability of ever occurring in an on-policy game.
Several? I can see one (the one you cite). Some of the other variants—e.g., no castling at all, or pawns can’t move two squares on their first move—can lead to positions that also arise in normal chess. But having neither side castle at all is really unusual and most such positions will be well out of distribution; and it’s very common for some pawns to remain on the second rank all the way to the endgame, where the option of moving one or two squares can have important timing implications.