I Was Wrong About Self-Improving Models: Here's What I Actually Found

This site does not allow itself to be embedded. You can still read it on the original site — the toolbar below keeps your place in the directory.

A follow-up to my retracted post “Self-Improving Models Without Labels: What I Just Proved and Why It Matters” The Retraction A few days ago, I published a post claiming I had proven that models could self-improve using only intrinsic fidelity signals. I was excited. The metrics looked great–fidelity scores improved by 18% over training cycles. I was wrong. When I actually tested…

Read on lab-stack.com ↗