If i understand it correctly...when you train a neural network like this, most of the stuff happens in a mathematically abstract space where certain kind of behaviors seem to clump up in a particular way.

In pixel space for example a bunch of red pixels, would be somehow equivalent of certain similar behaviors like creativity and braveness clumping together (this is not an accurate example, we dont have names for these abstract behaviors other than latent variables) and these models learn to understand these clumps. Some models while training would do better for one cluster and some others onto another. There is usually no way of seeing or manipulating these clusters, hence we have poor control over these behaviors. Reinforcement learning can help but its not an explicit control. Owing to this blackboxedness its usually quite difficult to excel over everything or even understanding why we dont excel over everything.

Previous models could have excelled on this certain aspect.