The Nonlinear Library LW - Evidence against Learned Search in a Chess-Playing Neural Network by p.b.
Sep 14, 2024
09:38
Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Evidence against Learned Search in a Chess-Playing Neural Network, published by p.b. on September 14, 2024 on LessWrong.
Introduction
There is a new paper and lesswrong post about "learned look-ahead in a chess-playing neural network". This has long been a research interest of mine for reasons that are well-stated in the paper:
Can neural networks learn to use algorithms such as look-ahead or search internally? Or are they better thought of as vast collections of simple heuristics or memorized data? Answering this question might help us anticipate neural networks' future capabilities and give us a better understanding of how they work internally.
and further:
Since we know how to hand-design chess engines, we know what reasoning to look for in chess-playing networks. Compared to frontier language models, this makes chess a good compromise between realism and practicality for investigating whether networks learn reasoning algorithms or rely purely on heuristics.
So the question is whether Francois Chollet is correct with transformers doing "curve fitting" i.e. memorisation with little generalisation or whether they learn to "reason". "Reasoning" is a fuzzy word, but in chess you can at least look for what human players call "calculation", that is the ability to execute moves solely in your mind to observe and evaluate the resulting position.
To me this is a crux as to whether large language models will scale to human capabilities without further algorithmic breakthroughs.
The paper's authors, which include Erik Jenner and Stuart Russell, conclude that the policy network of Leela Chess Zero (a top engine and open source replication of AlphaZero) does learn look-ahead.
Using interpretability techniques they "find that Leela internally represents future optimal moves and that these representations are crucial for its final output in certain board states."
While the term "look-ahead" is fuzzy, the paper clearly intends to show that the Leela network implements an "algorithm" and a form of "reasoning".
My interpretation of the presented evidence is different, as discussed in the comments of the original lesswrong post. I argue that all the evidence is completely consistent with Leela having learned to recognise multi-move patterns. Multi-move patterns are just complicated patterns that take into account that certain pieces will have to be able to move to certain squares in future moves for the pattern to hold.
The crucial different to having learned an algorithm:
An algorithm can take different inputs and do its thing. That allows generalisation to unseen or at least unusual inputs. This means that less data is necessary for learning because the generalisation power is much higher.
Learning multi-move patterns on the other hand requires much more data because the network needs to see many versions of the pattern until it knows all specific details that have to hold.
Analysis setup
Unfortunately it is quite difficult to distinguish between these two cases. As I argued:
Certain information is necessary to make the correct prediction in certain kinds of positions. The fact that the network generally makes the correct prediction in these types of positions already tells you that this information must be processed and made available by the network. The difference between lookahead and multi-move pattern recognition is not whether this information is there but how it got there.
However, I propose an experiment, that makes it clear that there is a difference.
Imagine you train the model to predict whether a position leads to a forced checkmate and also the best move to make. You pick one tactical motive and erase it from the checkmate prediction part of the training set, but not the move prediction part.
Now the model still knows which the right moves are to make i.e. it would pl...
Introduction
There is a new paper and lesswrong post about "learned look-ahead in a chess-playing neural network". This has long been a research interest of mine for reasons that are well-stated in the paper:
Can neural networks learn to use algorithms such as look-ahead or search internally? Or are they better thought of as vast collections of simple heuristics or memorized data? Answering this question might help us anticipate neural networks' future capabilities and give us a better understanding of how they work internally.
and further:
Since we know how to hand-design chess engines, we know what reasoning to look for in chess-playing networks. Compared to frontier language models, this makes chess a good compromise between realism and practicality for investigating whether networks learn reasoning algorithms or rely purely on heuristics.
So the question is whether Francois Chollet is correct with transformers doing "curve fitting" i.e. memorisation with little generalisation or whether they learn to "reason". "Reasoning" is a fuzzy word, but in chess you can at least look for what human players call "calculation", that is the ability to execute moves solely in your mind to observe and evaluate the resulting position.
To me this is a crux as to whether large language models will scale to human capabilities without further algorithmic breakthroughs.
The paper's authors, which include Erik Jenner and Stuart Russell, conclude that the policy network of Leela Chess Zero (a top engine and open source replication of AlphaZero) does learn look-ahead.
Using interpretability techniques they "find that Leela internally represents future optimal moves and that these representations are crucial for its final output in certain board states."
While the term "look-ahead" is fuzzy, the paper clearly intends to show that the Leela network implements an "algorithm" and a form of "reasoning".
My interpretation of the presented evidence is different, as discussed in the comments of the original lesswrong post. I argue that all the evidence is completely consistent with Leela having learned to recognise multi-move patterns. Multi-move patterns are just complicated patterns that take into account that certain pieces will have to be able to move to certain squares in future moves for the pattern to hold.
The crucial different to having learned an algorithm:
An algorithm can take different inputs and do its thing. That allows generalisation to unseen or at least unusual inputs. This means that less data is necessary for learning because the generalisation power is much higher.
Learning multi-move patterns on the other hand requires much more data because the network needs to see many versions of the pattern until it knows all specific details that have to hold.
Analysis setup
Unfortunately it is quite difficult to distinguish between these two cases. As I argued:
Certain information is necessary to make the correct prediction in certain kinds of positions. The fact that the network generally makes the correct prediction in these types of positions already tells you that this information must be processed and made available by the network. The difference between lookahead and multi-move pattern recognition is not whether this information is there but how it got there.
However, I propose an experiment, that makes it clear that there is a difference.
Imagine you train the model to predict whether a position leads to a forced checkmate and also the best move to make. You pick one tactical motive and erase it from the checkmate prediction part of the training set, but not the move prediction part.
Now the model still knows which the right moves are to make i.e. it would pl...