So far, the main idea I have pulled from the Lc0 crowd is to have a prior temperature indeed. The next thing I am planning to add is the possibility to batch inference requests across game simulations instead of relying on asynchronous MCTS. In your blog series, you anticipate the problem of the virtual loss introducing some exploration bias in the search but ultimately concludes that it does not change much:

[Citation from your blog series]: "Technically, virtual loss adds some degree of exploration to game playouts, as it forces move selection down paths that MCTS may not naturally be inclined to visit, but we never measured any detrimental (or beneficial) effect due to its use."

Interestingly, it seems that the LC0 team had a different experience here. I myself ran some tests and going from 32 to 4 workers (for 600 MCTS simulations per turn) on my connect-four agent results in a significant increase in performances. This may be due to the fact that I use a much smaller neural network than yours, which is ultimately not as strong.

Related to this, there is a question I have wanted to ask you since I found your blog article series: did you make experiments with smaller networks and what were the results? What is the smallest architecture you tried and how did it perform?

For example: did you notice than increasing or decreasing network size required significant changes in other hyperparameters? Are small networks learning faster at the beginning of training before they start to plateau?