Comment by physix

Comment by physix 20 hours ago

2 replies

I'm not sufficiently familiar with the details on ML to assess the proposition made in the article.

From my understanding, RL is a tuning approach on LLMs, so the outcome is still the same kind of beast, albeit with a different parameter set.

So empirically, I actually thought that the lead companies would already be strongly focused on improving coding capabilities, since this is where LLMs are very effective, and where they have huge cashflows from token consumptions.

So, either the motivation isn't there, or they're already doing something like that, or they know it's not as effective as the approaches they already have.

I wonder which one it is.

sva_ 20 hours ago

> From my understanding, RL is a tuning approach on LLMs,

What you're referring to is actually just one application of RL (RLHF). RL itself is much more than that

  • physix 7 hours ago

    Actually I didn't. Correct me if I am wrong, but my understanding is that RL is still an LLM tuning approach, i.e. an optimization of its parameter set, no matter if it's done at scale or via HF.