The AlphaZero research team states
A move in chess may be described in two parts: selecting the piece to move, and then selecting among the legal moves for that piece. We represent the policy π(a|s) by a 8 × 8 × 73 stack of planes encoding a probability distribution over 4,672 possible moves. Each of the 8×8 positions identifies the square from which to “pick up” a piece.
I am wondering if it would be better to only have the network output moves that could exist (i.e., "a1b3" is a possible knight move, but "a1g3" could never be reached by a piece). The modified output would be much smaller, and could potentially make the neural network learn quicker, right?