Comment by frumiousirc | Hacker News Reader

Comment by frumiousirc | Hacker News Reader

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My initial thought as well. But, what is the "Discriminator" here? What grounds the training toward reality? The "Challenger" and "Solver" adversity alone can only serve to amplify hallucination.

Ahh, GPT-4o is the arbiter.

So, basically, this is a way to perform LLM model compression (GPT-4o to qwen3) while maximizing the in-distribution domain size. As such, it seems reasonable and useful.

However the reliance on an arbiter LLM makes the claim that it will overcome the problem of a lack of training data unreasonable. Once the target LLM is scaled up to reach the in-distribution domain size of the arbiter, it seems to me it will turn back into a hallucination amplifier.

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).

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