Comment by thrw045

Maybe someone can clarify this:

He says:

> A reasoning model is essentially an upgrade from a conventional LLM, that is, adding more post-training via reinforcement learning with verifiable rewards (and optionally inference scaling techniques) to make it more capable of solving more challenging problems.

But this isn't really true is it? When reasoning models came around with OpenAI's o1, the main feature it had was to be able to try several paths and select the best one. This is what a reasoning model is. This was before agents and reinforcement learning with verifiable rewards wasn't it? And then RLVR and agents were added on later. Or am I wrong?