That's not my experience, the model is adding tons of unnecessary details, that make it much harder to find the important information for an expert that already knows all those details.
I don't want ELI5, I just want the model to go straight to the point, like a colleague human expert would do.
Why do you assume this order? I would assume the AI starts with lean (that's what it was RL'd on at least) then tried to translate the proof for us humans, which is quite hard.
I'm not sure about it but it seems plausible given the potential error.
Edit: they explicite say that your order is correct in the post
LLM inference has two very different regimes of work: prefill & decode. You can think of the former roughly as processing a pre-specified prompt, and the latter as sequential processing (auto-regressive token generation) eg. "chain of thought". The latter is very important for LLMs and cannot be ignored; it deeply influences infra design, even necessitates copious amounts of high-bandwidth memory. Jev-like models can ignore the latter and therefore optimize much better for the former, consequently operating at both better cost and latency.
And furthermore, because the model is forced to answer in a boolean (if in boolean mode), if the user input is outside of the range of a boolean, it's forced to hallucinate. It can't abstain.
The technology and the results are very handwavy. What is RLCD exactly ? What are scores on benchmarks compared to LLMs ?
This website does not inspire confidence at all, it all sounds like a marketing piece. I wish it was true, some kind of text-prompted classifier with LLM performance would be cool, but I can't trust it with what we are given.
We didn't have the compute required (GPUs powerful enough to parallelize forward and backward pass). This compute is what allows us to train from human knowledge or distillation.
I don't want ELI5, I just want the model to go straight to the point, like a colleague human expert would do.