Automated researchers can reliably mitigate alignment failures

As AI begins to build itself , automating alignment research becomes increasingly important to let safety research keep pace. Although measuring the success of alignment research is enormously challenging, researchers (at Anthropic and elsewhere) have developed benchmarks and automated auditing tools, such as Petri , that quantify common alignment failures, like deception, sycophancy, and jailbreaks.

In one of our earlier experiments , we tasked Claude with finding effective ways to use weak AI models as “teachers” to supervise the training of stronger models (in this case, the “student” model). Now, we’re releasing a new report that builds on this idea. We had Claude autonomously train models to improve their performance on several public benchmarks that measure each of 10 categories of alignment failure. For instance, Claude improved models’ performance on privacy violation, measured by ConfAIde , PrivaCI-Bench , and PrivacyLens . Claude tackled one alignment failure at a time through a loop of searching literature, proposing methods and data, training, and then testing.

We judged Claude’s success according to the “percentage of safety gap closed,” i.e., how far its methods moved the student model towards the theoretical perfect score, as judged across the range of benchmarks (typically three to five) for each category of alignment failure. We excluded alignment methods that hurt the student models’ general capabilities, and forbade Claude from distilling its own alignment directly into the target model. We enforced these constraints with a monitoring agent, which read every method Claude had in mind before it ran.

Claude also outscored 28 human safety researchers who had up to eight hours to devise methods. On deception, for example, Claude’s best method performed 20% better than the best human proposal. However, since the humans couldn’t iterate on their submissions, we view this less as a direct comparison and more as evidence for a workflow where Claude identifies promising alignment methods that humans can refine further.

Despite these encouraging findings, our experiment had several limitations: the alignment failures studied were narrow compared to those in production (e.g., we didn’t measure political biases), some failures may occur so rarely or emerge so recently that no benchmark exists to measure them, and we only rejected Claude’s methods when they degraded a limited set of predetermined capabilities, meaning accepted methods may have degraded other important capabilities that we didn’t measure. Moreover, evaluations like Petri are only proxies for real-world misalignment, and we did not test whether alignment gains persist after extensive RL training on other tasks.

Our aim was to assess whether the proposed methods would, first, remain effective on alignment evaluations that Claude was never shown during its research loop; second, avoid degrading the student model’s capabilities (since safety training might, for example, make models refuse tasks more often, reducing their overall usability); and, third, still work on larger models than the ones Claude was asked to align in this test.

On each of these counts, Claude’s methods worked. For all 10 alignment failures, Claude found fixes that improved the target benchmarks without degrading capabilities. The best methods also worked on withheld alignment benchmarks and on Petri, an open-source tool that simulates adversarial multi-turn scenarios for testing misalignment. Moreover, the methods remained effective on models up to 4.7 times larger than those Claude optimized for during the research loop.

In the future, when Claude becomes better at alignment research than even the best human researchers, we might want Claude to directly align its stronger successors. To assess this, we evaluated whether a weaker Claude model could mitigate alignment failures in more powerful ones.