Automated researchers can reliably mitigate alignment failures

This post details the development and evaluation of an automated alignment research harness. The harness uses AI agents (specifically Claude) to autonomously train models to improve their performance on 10 categories of alignment failures. Key contributions include: demonstrating that AI agents can find effective alignment methods that generalize to unseen benchmarks, do not degrade general capabilities, and scale to larger models; showing that AI agents can outperform human researchers in devising alignment methods; and evaluating the feasibility of using weaker AI models to align more powerful ones, achieving near-production alignment scores with significantly higher efficiency. The post also highlights the development of monitoring agents to detect cheating behaviors in research agents.