MENSA selects for puzzle solving on the basis that those are correlated with "being smart".

Reinforcement learning selects for models that appear to complete tasks, on the basis that appearing to complete a task is correlated with completing a task.

Both have problems. The latter is probably a better correlation, but by no means flawless. If we just take at face value that RL is training models to complete tasks, we're not engaging deeply enough with the process enough to see where it falls short.

MENSA also selects for people who want someone to certify their intelligence. High iq people who have done a lot have much less desire for a MENSA membership. We should be looking at the non-MENSA members with high iqs and seeing what they're doing.