Yeah the calibration is really what makes it useful in practice for quick, small decisions. Asking a LLM to give scores to a problem will yield inconsistently scaled/anchored results that changes at a whim.
The blog is pretty heavy on statistics. I'll have to study it more when I have time. Is it essentially bootstrapping results to statistically normalize the answers?
RLCD, not defined in the article, is Reinforcement Learning for Calibrated Decisions.
Ah, so not Reflective LCD [1] then.
[1] https://www.e3displays.com/reflective-lcd-display-monitor/
Yeah the calibration is really what makes it useful in practice for quick, small decisions. Asking a LLM to give scores to a problem will yield inconsistently scaled/anchored results that changes at a whim.
The blog is pretty heavy on statistics. I'll have to study it more when I have time. Is it essentially bootstrapping results to statistically normalize the answers?
Is this another Claude-ism? "X was always Y. The Z merely hid it." Or am I overcalling it?