Weak-to-strong generalization
There are nonetheless necessary disanalogies between our present empirical setup and the last word downside of aligning superhuman fashions. For instance, it could be simpler for future fashions to mimic weak human errors than for present robust fashions to mimic present weak mannequin errors, which may make generalization more durable sooner or later.
Nonetheless, we consider our setup captures some key difficulties of aligning future superhuman fashions, enabling us to start out making empirical progress on this downside as we speak. There are numerous promising instructions for future work, together with fixing the disanalogies in our setup, growing higher scalable strategies, and advancing our scientific understanding of when and the way we must always count on good weak-to-strong generalization.
We consider that is an thrilling alternative for the ML analysis group 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 as we speak.
- 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 how one can align future superhuman AI programs to be secure has by no means been extra necessary, and it’s now simpler than ever to make empirical progress on this downside. We’re excited to see what breakthroughs researchers uncover.
