Nine copies of Claude Opus 4.6 were equipped with sandbox environments and tasked to autonomously develop weak-to-strong supervision methods, scoring their progress by “performance gap recovered” (PGR). The AARs reached a PGR of 0.97 versus a human baseline of 0.23, showed partial generalization to new tasks, but failed to replicate gains at production scale, underscoring both the promise and limits of automated alignment experiments.

• Nine Claude Opus 4.6 copies working autonomously hit a PGR of 0.97 on weak-to-strong supervision, crushing the human baseline of 0.23, in just 5 days and ~$18,000.
• The top method didn't generalize well: it worsened coding performance on unseen tasks and showed no significant gain when tested on Sonnet 4 with production infrastructure, suggesting it overfit to quirks of the original setup.
• Giving each AAR a different, even vague, starting prompt outperformed rigid, uniform workflows by avoiding convergence on the same dead-ends.