CMSA New Technologies in Mathematics Seminar: Who Teaches the Teacher? Self-Improving Reasoning Systems

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AI reasoning has progressed at a startling pace: models have
moved from solving competition problems to producing genuinely new
mathematics, including breakthroughs on open questions that have
resisted human mathematicians for decades. Mathematics, however, has a
crucial advantage: progress can be verified.

In such domains, self-improvement increasingly looks possible. Models
can explore, learn from failures, and even generate curricula for other
copies of themselves—creating useful stepping stones beyond what they
can currently solve.

But verifiable domains may be the easy case. Most of the world provides
no theorem checker, unit test, or unambiguous reward. What should
reasoning—and self-improvement—mean without an external oracle?

I will discuss recent work on exploration, teacher–student learning, and
efficient reasoning in mathematical domains, and then turn to this
broader challenge: building systems that learn predictive models of the
world, reason and plan within them, and improve through the consequences
of their interactions with it.

Zoom link:https://harvard.zoom.us/j/95406961810  Passcode: cmsa