Annotations mitigate post-training mode collapse
Jacob Springer, Madhu Advani, Lukas Aichberger, Arwen Bradley, Eran Malach, Omid Saremi, Sinead Williamson, Preetum Nakkiran, Etai Littwin, Aditi Raghunathan
ICML 2026
I am a PhD student in the Machine Learning Department at Carnegie Mellon University, advised by Aditi Raghunathan, and supported by an NSF Graduate Research Fellowship.
I study how optimization shapes foundation models across pretraining, post-training, robustness, and inference-time behavior.
About
I'm excited about solving mysteries in machine learning. I'm broadly interested in the science of foundation models, with current work focused on optimization, robustness, and inference-time methods. Lately I've been thinking about how to train models that, by design, can be fine-tuned easily and robustly to new tasks, and how the choice of optimizer shapes that.
I did my undergrad at Swarthmore College, and have spent time at Cold Spring Harbor Laboratory, MIT, and Los Alamos National Laboratory, where I worked with many lovely people. Please reach out if you'd like to chat — I love talking about research.
Selected Publications
View all publications →Sharpness-aware pretraining mitigates catastrophic forgetting
Ishaan Watts, Catherine Li, Sachin Goyal, Jacob Springer†, Aditi Raghunathan†
ICML 2026 · Oral @ ICBINB ICLR 2026Overtrained language models are harder to fine-tune
Jacob Springer, Sachin Goyal, Kaiyue Wen, Tanishq Kumar, Xiang Yue, Sadhika Malladi, Graham Neubig, Aditi Raghunathan
ICML 2025 · Outstanding Paper @ SCOPE ICLR 2025 · Entropic Paper Award @ ICBINB ICLR 2025Repetition improves language model embeddings
Jacob Springer, Suhas Kotha, Daniel Fried, Graham Neubig, Aditi Raghunathan
ICLR 2025Sharpness-aware minimization enhances feature quality via balanced learning
Jacob Springer, Vaishnavh Nagarajan, Aditi Raghunathan
ICLR 2024