According to a blog post by DeepMind, AlphaZero doesn't have a real rollout.
AlphaGo Zero does not use "rollouts" - fast, random games used by other Go programs to predict which player will win from the current board position. Instead, it relies on its high quality neural networks to evaluate positions.
Instead, I assume it just interprets the winner at a given state by the NN values head result. This replaces the rollout. So the computation time saved could be used for many expansions instead. Evaluating a state from a root node would then be the best action derived from the visit count in MCTS, which is only based on the predictions of the NN value heads. (no current score, no policy?)
With policy, I mean the NN's policy head (softmax).
This would mean that the NN policy is only used in the loss calculation and nowhere else?