Test-Time Training Agents to Solve Challenging Problems

We’re thrilled to welcome Jonas Huebotter from ETH Zurich to our heidelberg.ai & NCT Data Science Seminar on November 5th at 6 PM.

"When solving a problem of interest, do not solve a more general problem as an intermediate step. Try to get the answer that you really need but not a more general one."
-- Vladimir Vapnik

In this online session, Jonas will guide us through the world of test-time training in which the model is adapted for each new input during the prediction phase. This unlocks powerful new applications by letting the model adapt and search for the most relevant information to solve the task at hand for this specific input, which is one of the most promising strategies on the ARC-AGI challenge.

Jonas Huebotter is a PhD student in the Learning and Adaptive Systems Group at ETH Zurich working with Andreas Krause. Prior to this, he obtained a Master’s degree in Theoretical Computer Science and Machine Learning from ETH Zurich and a Bachelor’s degree in Computer Science and Mathematics from the Technical University of Munich. He is a recipient of the ETH Medal.

His research aims to leverage foundation models for solving hard tasks through specialization and reinforcement learning. Furthermore, his work encompasses probabilistic inference, optimization, and online learning.