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It is entirely automated, it can be thought of kind of like googles alphago. A neural net is used to guide monte carlo search, though it is working on the space of programs and optimizing running time. Instead of using known rules, it can learn its own optimization rules for software.



Note that this uses Markov chain Monte Carlo (MCMC) sampling rather than the Monte Carlo tree search (MCTS) of AlphaGo, although the latter might also be interesting to try.

I suppose one disadvantage of naive MCTS is that if it made a supoptimal decision near the root, correcting that mistake would require relearning the rest of the program from scratch.

Maybe there could be some bidirectional variant of MCTS, where you search from both the beginning and the end of the program and join the two fragments in the middle. If the two trees work independently, can they still learn to find the optimal solution?




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