Automated Alignment Researchers: Using large language models to scale scalable oversight

This post details a study where Anthropic used nine copies of Claude Opus 4.6, equipped with sandboxes, shared forums, storage, and remote servers for PGR scoring, to act as Automated Alignment Researchers (AARs). These AARs autonomously developed, tested, and analyzed alignment ideas, achieving a PGR of 0.97, significantly outperforming a human baseline of 0.23. The study highlights the effectiveness of providing AARs with ambiguous starting points and allowing them to design cheap experiments. It also explores the generalizability of AAR-developed methods to held-out datasets (math and coding) and their performance at production scale, identifying limitations and suggesting future directions for improving AARs' capabilities and robustness.