3 ms·

I think you have it backwards. The common mistake is to think “maybe if we use a blend of raw data and hand-crafted heuristics, we’ll get the best of both worl

I think you have it backwards. The common mistake is to think “maybe if we use a blend of raw data and hand-crafted heuristics, we’ll get the best of both worlds!” But the bitter lesson says no, beyond a certain point it’s better just to use the data. Thinking that an LLM might be able to improve on purely “big data” machine learning seems to me to be the same incorrect idea. Its “intelligence” is no more useful than human intelligence. The LLM is based on a massive data corpus, sure, but the amount of data specifically about chess in there pales in comparison to just playing billions of games of chess.

TwelveEyes 1mo ago
Also, training it on chess books is literally training it on human knowledge, and not the actual game, which is exactly what the bitter lesson says not to do.
Dylan16807 1mo ago
> I think you have it backwards. > maybe if we use a blend of raw data and hand-crafted heuristics I don't follow. They're suggesting giving raw chess data to the LLM, no heuristics involved.
- iainmerrick 1mo ago
  I was replying to this: The conclusion of the bitter lesson would be that a large language model trained on chess commentary as well as being trained on millions of chess games would outperform stockfish which is only trained on millions of chess games. If you can draw any lessons from chess commentary, I think it’s very reasonable to call it “hand-crafted heuristics.”
  - Dylan16807 1mo ago
    Hand-crafted even if you're feeding in the raw commentary? That seems like a weird way to consider it. Wouldn't that make LLMs in general "hand-crafted"? And raw games plus raw commentary is all the data you have. You can make more games but those can be fed to both stockfish and the LLM competitor. So it seems like a valid interpretation of the bitter lesson to me.
    - iainmerrick 1mo ago
      Yeah, "hand-crafted" is a bit of a stretch; I mean their value is in the human insight they contain. The key point I was trying to get at is that the human insights don't contain anything that can't be mined from vast amounts of gameplay. Every human insight can eventually be rediscovered and made rigorous by data (in chess, at least!) In the short term, those insights are useful, but in the longer term, they add nothing at all. Note also that "raw gameplay" here can mean new games -- you can generate as much data as you need, you don't need to rely on real recorded games.
      - Dylan16807 1mo ago
        > Every human insight can eventually be rediscovered and made rigorous by data (in chess, at least!) In the short term, those insights are useful, but in the longer term, they add nothing at all. But isn't that the bulk of what we're shoving into LLMs, and it makes them much smarter? If it's useful there but not in a chess AI then that seems like a significant crack in the bitter lesson. > Note also that "raw gameplay" here can mean new games -- you can generate as much data as you need, you don't need to rely on real recorded games. Yeah I mentioned that, generated games are useful. But if we're being fair and letting both AIs use generated game data, does the more general LLM ever actually overtake the specialized stockfish like the bitter lesson suggests? Another way to look at this is that giving the LLM the commentary is a way to avoid complaints of hiding data from the LLM, since it'll have strictly more info than stockfish. But if we cut that from the training data and only give it a basic description of chess and lots of raw game data then it's going to get even worse than it already is at chess. Meanwhile stockfish never had that commentary, just actual hand-crafted heuristics and training on game data, and it's very strong.

TwelveEyes 1mo ago

Also, training it on chess books is literally training it on human knowledge, and not the actual game, which is exactly what the bitter lesson says not to do.

Dylan16807 1mo ago

> I think you have it backwards. > maybe if we use a blend of raw data and hand-crafted heuristics I don't follow. They're suggesting giving raw chess data to the LLM, no heuristics involved.

iainmerrick 1mo ago
I was replying to this: The conclusion of the bitter lesson would be that a large language model trained on chess commentary as well as being trained on millions of chess games would outperform stockfish which is only trained on millions of chess games. If you can draw any lessons from chess commentary, I think it’s very reasonable to call it “hand-crafted heuristics.”
- Dylan16807 1mo ago
  Hand-crafted even if you're feeding in the raw commentary? That seems like a weird way to consider it. Wouldn't that make LLMs in general "hand-crafted"? And raw games plus raw commentary is all the data you have. You can make more games but those can be fed to both stockfish and the LLM competitor. So it seems like a valid interpretation of the bitter lesson to me.
  - iainmerrick 1mo ago
    Yeah, "hand-crafted" is a bit of a stretch; I mean their value is in the human insight they contain. The key point I was trying to get at is that the human insights don't contain anything that can't be mined from vast amounts of gameplay. Every human insight can eventually be rediscovered and made rigorous by data (in chess, at least!) In the short term, those insights are useful, but in the longer term, they add nothing at all. Note also that "raw gameplay" here can mean new games -- you can generate as much data as you need, you don't need to rely on real recorded games.
    - Dylan16807 1mo ago
      > Every human insight can eventually be rediscovered and made rigorous by data (in chess, at least!) In the short term, those insights are useful, but in the longer term, they add nothing at all. But isn't that the bulk of what we're shoving into LLMs, and it makes them much smarter? If it's useful there but not in a chess AI then that seems like a significant crack in the bitter lesson. > Note also that "raw gameplay" here can mean new games -- you can generate as much data as you need, you don't need to rely on real recorded games. Yeah I mentioned that, generated games are useful. But if we're being fair and letting both AIs use generated game data, does the more general LLM ever actually overtake the specialized stockfish like the bitter lesson suggests? Another way to look at this is that giving the LLM the commentary is a way to avoid complaints of hiding data from the LLM, since it'll have strictly more info than stockfish. But if we cut that from the training data and only give it a basic description of chess and lots of raw game data then it's going to get even worse than it already is at chess. Meanwhile stockfish never had that commentary, just actual hand-crafted heuristics and training on game data, and it's very strong.