The ML in NYC Speaker Series + Happy Hour is excited to host Professor Greg Durrett as our November speaker! His talk will take place Thursday, November 6th, at 4pm at the Flatiron Institute. As always, there will be a reception afterward for all attendees.
Title: LLM Reasoning Beyond Scaling
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 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 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on current limitations including generator-validator gaps, poor compositional generalization, limited creativity, and outdated knowledge. In all cases, fundamental limitations of LLMs or of supervised learning in general make these problems challenging, inviting future study and novel solutions beyond scaling.
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 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 has served in numerous roles for ML and NLP conferences, recently as a member of the NAACL Board since 2024 and as Senior Area Chair for ACL 2025 and EMNLP 2025. 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.
