Reading Anthropic's Report on Claude Researching Fixes for Alignment Failures
Anthropic published a report on having Claude do its own research into reducing alignment failures. An alignment failure is when an AI behaves differently from what people intended, and the article's examples are deception, sycophancy and jailbreaks. This post follows the experiment and the plans that come after it.
Here is how the experiment ran.
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.
According to the article, scores went up in all 10 categories without degrading the models' general capabilities. In another test, Claude Sonnet 5 worked on an early version of the stronger Opus 4.8, tried over 50 solutions in 60 hours, and reached alignment scores close to the production models.
The article ends on its plans.
We plan to continue improving Claude’s ability to measure subtle failures, further study automating alignment post-training on production-grade models, and run more comprehensive analyses. Overall, we view these results as early positive signals that automated alignment post-training could become practical in the near term, and we will share updates as this work progresses.
Reading the whole thing, my impression was simply that this research on alignment failures is continuing. Nothing in particular stayed with me, and I'll just keep watching the results as they come out.
According to the article, the automated alignment research harness is open source. The detailed results are in the report on the Alignment Science blog.
