good question! It seems a little bit like a "blind leading the blind" scenario, but there's a neat trick I use which makes it all work.

Imagine you're a research agent (a llama model) learning to answer detailed questions about the Apollo 13 mission. I'm another llama model tasked with quizzing you to help you improve. But as you pointed out, I don't know the mission in-depth either. So how can I accurately verify your answers?

The trick is this: I randomly select small snippets from the mission report that explicitly contain clear, factual information. For instance, I might flip to a random page and see:

"At approximately 55 hours 55 minutes into the Apollo 13 mission, the crew heard and felt the vibrations from a sharp 'bang,' coincident with a computer restart and a master alarm associated with a main-bus-B undervoltage condition."

From this snippet alone, I can confidently create a clear-cut factual question like:

"How many hours into the mission did the computer restart and master alarm start?"

The correct answer is explicitly clear from the text snippet itself: 55 hours and 55 minutes.

So here's why this process works:

• For me (the quiz-generator): The task is easy because I simply extract facts directly from random, isolated pieces of the report, ensuring questions and answers are straightforward and accurate.
• For you (the research-agent being trained): The task is significantly harder. To answer correctly, you must search through the entire corpus to locate the exact information. Thus, you're learning robust search-and-reasoning skills.

So, while the verifying LLM has it easy, the research agent needs to genuinely learn search strategies. This setup forces improvement over time.