There are still important disanalogies between our current empirical setup and the ultimate problem of aligning superhuman models. For example, it may be easier for future models to imitate weak human errors than for current strong models to imitate current weak model errors, which could make generalization harder in the future.
Nevertheless, we believe our setup captures some key difficulties of aligning future superhuman models, enabling us to start making empirical progress on this problem today. There are many promising directions for future work, including fixing the disanalogies in our setup, developing better scalable methods, and advancing our scientific understanding of when and how we should expect good weak-to-strong generalization.
We believe this is an exciting opportunity for the ML research community to make progress on alignment. To kickstart more research in this area,
- We are releasing open source code to make it easy to get started with weak-to-strong generalization experiments today.
- We are launching a $10 million grants program for graduate students, academics, and other researchers to work on superhuman AI alignment broadly. We’re especially excited to support research related to weak-to-strong generalization.
Figuring out how to align future superhuman AI systems to be safe has never been more important, and it is now easier than ever to make empirical progress on this problem. We are excited to see what breakthroughs researchers discover.
