Anthropic Tests AI That Grades Its Own Homework

Anthropic has released a paper titled “Automated Researchers Can Reliably Mitigate Alignment Failures,” detailing a system designed to improve AI model performance on alignment benchmarks without human intervention, as TechCrunch first reported.

The Automated Alignment Researcher (AAR), led by Anthropic fellow Chen Yueh-Han, is designed to mirror traditional research workflows. The system scans existing literature, proposes methods, and trains models in 30-minute increments. According to TechCrunch, the AAR successfully improved performance across 10 specific misaligned behavior benchmarks without degrading overall performance.

The paper explicitly compares the AAR to human researchers, claiming the best AAR methods outperform human proposals on average within six hours. The efficiency gap is further highlighted by a cost analysis cited in the paper: the AAR costs approximately $4 per hour in API inference, whereas human researchers cost roughly $150 per hour.

While the paper suggests that automated alignment post-training could be practical in the near term, it acknowledges critical dependencies. The system's efficacy relies entirely on the accuracy of the benchmarks used to reflect alignment goals. Additionally, the paper notes that significant work remains regarding the maintenance and expansion of the literature the AAR utilizes, as well as the establishment of the benchmarks themselves.