LLMs learn a distribution during pre-training, not only an average.

Then, by giving them context or by post-training, you can make them sample non-average parts of the distribution they learned.

> Then, by giving them context or by post-training, you can make them sample non-average parts of the distribution they learned.

How do you derive that something is "below average" or "average" or "above average"?

	▲	rytill 2 hours ago \| parent \| next [-]
		Well, it’s up to the user or post-trainer of the LLM what they believe to be above average. Then they can design around that. In the case of real world LLMs and post-training, what is above average is defined roughly as: labeled good by expert humans, and scoring high on RL environments related to coding like debugging, passing tests, or running efficiently and verifiably correctly.
	▲	nextaccountic 2 hours ago \| parent \| prev [-]
		> How do you derive that something is "below average" or "average" or "above average"? One technique is RLHF: have an human expert assess it.