I should have been more specific then. The problem isn't that the search space is too large to explore. The problem is that the search space is so large that the training procedure actively prefers to restrict the search space to maximise short term rewards, regardless of hyperparameter selection. There is a tradeoff here that could be ignored in the case of chess, but not for general math problems.

This is far from unsolvable. It just means that the "apply RL like AlphaGo" attitude is laughably naive. We need at least one more trick.