> To better estimate latency, we implemented a dual value function setup, whereby AlphaDev has two value function heads: one predicting algorithm correctness and the second predicting algorithm latency. The latency head is used to directly predict the latency of a given program by using the program’s actual computed latency as a Monte Carlo target for AlphaDev during training. This dual-head approach achieved substantially better results than the vanilla, single head value function setup when optimizing for real latency.
Briefly, they use a neural network to predict whether a given sequence of instructions is correct, and how fast it is. Then they used this neural network to guide the program generation via Monte Carlo tree search [1]. It is this procedure that keeps track of the partial score functions at each node.
> To better estimate latency, we implemented a dual value function setup, whereby AlphaDev has two value function heads: one predicting algorithm correctness and the second predicting algorithm latency. The latency head is used to directly predict the latency of a given program by using the program’s actual computed latency as a Monte Carlo target for AlphaDev during training. This dual-head approach achieved substantially better results than the vanilla, single head value function setup when optimizing for real latency.
Briefly, they use a neural network to predict whether a given sequence of instructions is correct, and how fast it is. Then they used this neural network to guide the program generation via Monte Carlo tree search [1]. It is this procedure that keeps track of the partial score functions at each node.
[1] https://en.wikipedia.org/wiki/Monte_Carlo_tree_search