LLM Reasoning Beyond Scaling
Presenter: Dr. Greg Durrett, Associate Professor, Department of Computer Science and the Center for Data Science, New York University
Abstract: Large reasoning models have demonstrated capabilities to solve competition-level math problems, answer “deep research” questions, and address complex coding needs. Much of this progress has been enabled by the scaling of data: pre-training data to learn vast knowledge, fine-tuning data to learn natural language reasoning, and RL environments to refine that reasoning. In this talk, I will describe the current LLM reasoning paradigm, its boundaries, and the future of LLM reasoning beyond scaling. First, I will describe the state of reasoning models and where I think scaling can lead to some additional (though perhaps limited) successes. I will then shift to discussing more fundamental issues with models that scale will not resolve in the next few years. I will touch on current limitations for long-running AI agents like Claude Code and where I see this technology progressing in the near future.
Speaker Bio: Greg Durrett is an associate professor in the Department of Computer Science and the Center for Data Science at New York University. His research is broadly in the areas of natural language processing and machine learning. Currently, his group's focus is on reasoning about knowledge in text, verifying the correctness of generation methods, and studying how to make progress on problems that defy LLM scaling. He is a 2023 Sloan Research Fellow and a recipient of a 2022 NSF CAREER award. He received his BS in Computer Science and Mathematics from MIT and his PhD in Computer Science from UC Berkeley, where he was advised by Dan Klein
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Speaker: Dr. Judy Fox, Associate Professor, School of Data Science, University of Virginia
Abstract:
Speaker Bio: Judy Fox is an Associate Professor of Data Science and holds a courtesy appointment as Associate Professor with the Department of Computer Science. Her research focuses on designing experimental systems that enable Data Science, Machine Learning, and the Internet of Things, applications to harness the computational resources of Cloud and HPC platforms effectively. Prior to joining the School of Data Science in 2021, Fox worked at Indiana University as the Director of Graduate Studies of Data Science and Associate Professor of Computer Science. In her research, Fox has earned three educational funding awards, including those from Google, NIH, and NSF, to develop new curricula in emerging areas to address national big data challenges and workforce development. She values the importance of interdisciplinary research, as her work has intersected with a variety of fields.
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Venue: Room 201, Astronomy Building, University of Virginia.
Date: Wednesday, March 04, 2026.