AIR Weekly Seminar: Shrimai Prabhumoye

Speaker:Shrimai Prabhumoye

Talk Title: "From Next-Token Prediction to Next-Generation Intelligence: The Future of Pretraining"

Abstract: This talk presents recent progress in pretraining algorithm design for large language models (LLMs), emphasizing the role of data ordering, reasoning-centric data integration, and reinforcement-based objectives in shaping model capability. We introduce a two-phase pretraining framework that formalizes strategies for data selection, blending, and sequencing, and demonstrate that front-loading reasoning-rich data during pretraining yields persistent gains in reasoning accuracy that post-training alone cannot reproduce. Further, we propose Reinforcement Learning during Pretraining (RLP) — a reinforcement based objective that treats chain-of-thought generation as exploratory behavior, rewarding trajectories that maximize token-level information gain. Empirical results across diverse model scales show that structured data ordering combined with RLP improves downstream accuracies, and reasoning generalization, establishing a principled approach for integrating reasoning as a first-class objective in LLM pretraining.

Bio: Shrimai Prabhumoye is a Senior Research Scientist at NVIDIA’s Applied Deep Learning Research Group and an Adjunct Professor at Boston University. Her work advances the state of large language models (LLMs) through innovations in pretraining algorithms that emphasize data selection, blending, and ordering strategies, as well as the use of reinforcement-based objectives to instill foundational reasoning abilities. She is the lead contributor to NVIDIA’s Nemotron family of models, driving progress in data and reasoning. Shrimai earned her PhD in Computer Science from Carnegie Mellon University, where she focused on controllable text generation and computational ethics. Her research has been featured in VentureBeat, Forbes, and TechCrunch.

Faculty Host: Bryan Plummer

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