Leaving aside the issue of to what extent the NN itself is already doing something approximately isomorphic to search or how easy it would be to swap in MuZero instead, I think that the important thing is to measure the benefit of search in particular problems (like Jones does by sweeping over search budgets vs training budgets for Hex etc) rather than how hard the exact algorithm of search itself is.
I mean, MCTS is a simple generic algorithm; you can just treat learning it in a ‘neural’ way as a fixed cost—there’s not much in the way of scaling laws to measure about the MCTS implementation itself. MCTS is MCTS. You can plug in chess as easily as Go or Hex.
It seems much more interesting to know about how expensive ‘real’ problems like Hex or Go are, how well NNs learn, how to trade off architectures or allocate compute between train & runtime...
Leaving aside the issue of to what extent the NN itself is already doing something approximately isomorphic to search or how easy it would be to swap in MuZero instead, I think that the important thing is to measure the benefit of search in particular problems (like Jones does by sweeping over search budgets vs training budgets for Hex etc) rather than how hard the exact algorithm of search itself is.
I mean, MCTS is a simple generic algorithm; you can just treat learning it in a ‘neural’ way as a fixed cost—there’s not much in the way of scaling laws to measure about the MCTS implementation itself. MCTS is MCTS. You can plug in chess as easily as Go or Hex.
It seems much more interesting to know about how expensive ‘real’ problems like Hex or Go are, how well NNs learn, how to trade off architectures or allocate compute between train & runtime...