Comment by rytill
2 days ago
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.
2 days ago
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"?
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.
> How do you derive that something is "below average" or "average" or "above average"?
One technique is RLHF: have an human expert assess it.
Mhm, I just wonder how many samples they get and how much time they have to come to the conclusion.
Like a short example is easier to grade, but not in the same ballpark as a whole codebase.
>How do you derive that something is "below average" or "average" or "above average"?
How do you? I mean, that was your point basis.