I wonder what the role of RLHF is in this. It seems to be one of the more labor-intensive, proprietary, dark-matter aspects of the LLM training process.

Just like some humans may be conditioned by education to assume that all questions posed in school are answerable, RLHF might focus on "happy path" questions where thinking leads to a useful answer that gets rewarded, and the AI might learn to attempt to provide such an answer no matter what.

What is the relationship between the system prompt and the prompting used during RLHF? Does RLHF use many kinds of prompts, so that the system is more adaptable? Or is the system prompt fixed before RLHF begins and then used in all RLHF fine-tuning, so that RLHF has a more limited scope and is potentially more efficient?