I'm still trying to grok and implement the paper, but I studied AlphaGo/AlphaZero/MuZero during my PhD. The core contribution here is the Nash equilibrium component to imperfect information games using only self-play. Note, there is no MCTS being done in this paper. This differs from counter factual regret methods (like the most famous Poker AIs) because it does not need to compute for all possible "information sets" which makes it intractable for even sufficiently complicated poker variants. It should also be noted (as they do in the paper) that this is more incremental than methodologically innovative as AlphaGo. This is the AlphaZero step increment to NeuRD. As is my general critique with their previous papers, they generally omit many engineering details that prove to be very important. Here, they admit that fine-tuning is vitally important (one of the 3 core steps) but details are relegated to the supplementary materials. It also opens up the question of if this new "fine-tuned" policy still guarantees the Nash equilibrium which it obviously does not as some mixed strategies are going to have sufficiently small probability. I wish researchers would be more honest with "this is a hack to get things to work on a computer because neural networks have floating point inaccuracies". It doesn't ruin any of the theory and no one is going to hold it against you. But it causes all sorts of confusion when trying to reimplement.