AI

Weak-to-strong generalization

There are nonetheless essential disanalogies between our present empirical setup and the final word drawback of aligning superhuman fashions. For instance, it might be simpler for future fashions to mimic weak human errors than for present robust fashions to mimic present weak mannequin errors, which might make generalization more durable sooner or later. 

However, we imagine our setup captures some key difficulties of aligning future superhuman fashions, enabling us to begin making empirical progress on this drawback right this moment. There are numerous promising instructions for future work, together with fixing the disanalogies in our setup, creating higher scalable strategies, and advancing our scientific understanding of when and the way we should always anticipate good weak-to-strong generalization.

We imagine that is an thrilling alternative for the ML analysis neighborhood to make progress on alignment. To kickstart extra analysis on this space,

  • We’re releasing open source code to make it straightforward to get began with weak-to-strong generalization experiments right this moment.
  • We’re launching a $10 million grants program for graduate college students, teachers, and different researchers to work on superhuman AI alignment broadly. We’re particularly excited to assist analysis associated to weak-to-strong generalization.

Determining find out how to align future superhuman AI methods to be protected has by no means been extra essential, and it’s now simpler than ever to make empirical progress on this drawback. We’re excited to see what breakthroughs researchers uncover.

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