Weak-to-strong generalization

By neub9
2 Min Read

Despite important disanalogies between our current empirical setup and the ultimate problem of aligning superhuman models, there is potential for future models to imitate weak human errors more easily than for current strong models to imitate current weak model errors, which could complicate generalization in the future. 

Nevertheless, 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 resolving 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 see this as an exciting opportunity for the ML research community to make progress on alignment. To encourage more research in this area,

  • We are releasing open source code to facilitate starting 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 are particularly eager to support research related to weak-to-strong generalization.

Understanding how to align future superhuman AI systems and making them safe has never been more crucial, and it is now more accessible than ever to make empirical progress on this problem. We are excited to witness the breakthroughs that researchers will discover.

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