See Figure 2.

The solver/challenger is the GAN discriminator/generator.

The challenger is trained to create difficult questions. The solver is trained to strengthen pathways that correctly solve the questions like so:

> To guide the Challenger toward producing challenging yet solvable questions, we first define an uncertainty score. For a generated question x, we query the current Solver... The most frequent response is treated as the pseudo-label y˜(x), and we compute the Solver’s empirical accuracy....The uncertainty reward is then defined.... This function incentivizes questions where the Solver is maximally uncertain (accuracy approaches 50%)

Identifying the best pseudo-label seems like it would be the limitation of the approach.

> Identifying the best pseudo-label seems like it would be the limitation of the approach.

Yes, I think this says in a different way what I'm trying to express.

In GAN, the Discriminator pegs the training to some chosen reality (assuming the "real" data set is truly real). In Challenger/Solver alone, there is no peg. The Solver could hallucinate consistently and "win" the race. It's the consistency that is the goal.

With GPT-4o as an arbiter of the Challenger/Solver training it provides the reality peg (or rather, the peg that biases toward GPT-4o's training set).