RLVR generally upweights tokens along the whole thinking trace that led to a correct answer, whether each token was "correct" or not. RLVR doesn't train a model to output an 80% likelihood, it just trains it to produce correct answers, and not to produce incorrect ones.

System One hasn't said how RLCD works, but they do say it is explicitly training models to output "calibrated" probabilities, which makes it distinct from RLVR. This is how they describe it:

> System One models are trained for calibrated decisions: their probabilities are optimized against outcomes to reflect uncertainty.

In RLCD (which is now an RL acronym that has 3 different unrelated expansions!), you basically massively negatively reward a distribution that is {yes: 0.9, no: 0.1} if the answer was no, and less negatively reward a {yes: 0.6, no: 0.4}. Many nuances when designing the details, but that is the rough idea.

It is a known existing thing variously called "calibrated RL" or such.

Implementing it on top of LLMs was difficult to get it to work, they seem to have done it up so its good enough for a polished product that works in a wide variety of usecases at the same time. I got accepted from the waitlist and it's really neat. Edit: it is now on vercel gateway.

One thing to note, the out of distribution behaviour will be different from what we are used to with regular LLMs. Theoretically, it should be worse, but practically, it depends on their method.