> The first thing to note about traditional SFT is that the responses in the examples are typically human written. ... But it is also possible to build the dataset using responses from the model we’re about to train. ... This is called Rejection Sampling.
I can see why someone might say there's overlap between RL and SFT (or semi-supervised FT), but how is "traditional" SFT considered RL? What is not RL then? Are they saying all supervised learning is a subset of RL, or only if it's fine tuning?
You seem to have a very narrow view of what is a relevant or a valid comment. Just because a counterargument doesn't completely refute the original comment, or "introduces" new concepts, doesn't make it irrelevant or "misdirection".
Someone compared treatment of X 20 years ago to treatment of Y today -- seems pretty natural to bring up treatment of X more recently. You can't just say "the original comment didn't mention it so you can't mention it either".
I don't see how your accusations of bad faith are warranted.
It's still pretty meaningless unless you have some idea what the previous profit was (which the average headline reader probably doesn't). 95% of last quarter's profit could be a trivial amount or a huge amount. If last quarter's profit was $100 and this quarter's was $5, describing it as a "plunge" is misleading as it hasn't really changed.
> ... SFT is a subset of RL.
> The first thing to note about traditional SFT is that the responses in the examples are typically human written. ... But it is also possible to build the dataset using responses from the model we’re about to train. ... This is called Rejection Sampling.
I can see why someone might say there's overlap between RL and SFT (or semi-supervised FT), but how is "traditional" SFT considered RL? What is not RL then? Are they saying all supervised learning is a subset of RL, or only if it's fine tuning?