Jonas Hübotter
PhD Student at ETH Zurich. I work on Test-Time Training and Reinforcement Learning.

I am a PhD student in the Learning and Adaptive Systems Group at ETH Zurich working with Andreas Krause. Prior to this, I 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. I am a recipient of the ETH Medal.
My research aims to leverage foundation models for solving hard tasks through specialization and reinforcement learning. Beyond this, I have broad interests including (approximate) probabilistic inference, optimization, and online learning.
Always feel free to reach out to me with things you find exciting.
Contacts: jhuebotter@ethz.ch Google Scholar GitHub Linkedin
Announcements
Jul, 2025 | COLM 2025: Local Mixtures of Experts: Essentially Free Test-Time Training via Model Merging has been accepted! We will also present our work on test-time scaling via prefix-confidence at the SCALR workshop. |
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May, 2025 | ICML 2025: Active Fine-Tuning of Multi-Task Policies has been accepted! We will also present our work on test-time offline RL at the PUT workshop and our work on curricula for sparse-reward RL at the EXAIT workshop. |
Feb, 2025 | Very excited to share notes on Probabilistic AI that I have been writing with Andreas Krause! |
Jan, 2025 | ICLR 2025: Efficiently Learning at Test-Time: Active Fine-Tuning of LLMs has been accepted! |
Jan, 2025 | AISTATS 2025: LITE: Efficiently Estimating Gaussian Probability of Maximality has been accepted! |
Selected Publications
Latest Talks
→ all talks Jan 13, 2026 Invited Talk | Test-Time Training Agents for Deep Exploration BLISS Speaker Series, Berlin |
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Nov 18, 2025 Invited Lecture | Test-Time Training and Adaptation “Foundation Models and Generative AI” (hosted by Prof. Charlotte Bunne), EPFL, Lausanne |
Nov 5, 2025 Invited Talk | Test-Time Training Agents to Solve Challenging Problems heidelberg.ai Speaker Series (hosted by Carsten Lüth), Heidelberg |
Aug 12, 2025 Invited Talk | Test-Time Training for Hard Tasks Google Paradigms of Intelligence team (hosted by Johannes von Oswald), Zurich |
Jul 10, 2025 Invited Talk | Towards Solving Hard Problems via Test-Time Training 📝 1st Prague Workshop on Neural Networks and Reasoning, Prague |
Supervision
I have had the privilege of advising several BSc and MSc students during their theses and semester projects. Some of these projects have led to publications.
- Dennis Jüni (MSc): Meta Test-Time Training for Image Classification (with Frederike Lübeck)
- Matthias Otth (MSc): Efficient Fine-Tuning and Test-Time Training of Large Language Models for Reasoning Tasks (with Ido Hakimi, SCALR@COLM '25)
- Leander Diaz-Bone (MSc): Directed Goal-Conditioned Reinforcement Learning (with Marco Bagatella)
- Ryo Bertolissi (BSc): Test-Time Model Merging for Mixture of Local Experts (with Ido Hakimi, COLM '25)
- Nicolas Menet (MSc): Efficiently Estimating Gaussian Probability of Maximality (with Parnian Kassraie, AISTATS '25)
- Sascha Bongni (BSc): Active Fine-Tuning of Large Language Models (ICLR '25)
- Pablo Lahmann (MSc): Safe Control as Inference (with Yarden As)
- Anh Duc Nguyen (BSc): Safe Bayesian Optimization without Regret