3 papers
cs.LG2025
Characterization and Mitigation of Training Instabilities in Microscaling Formats
Huangyuan Su, Mujin Kwun, Stephanie Gil +2
Training large language models is an expensive, compute-bound process that must be repeated as models scale, algorithms improve, and new data is collected. To address this, next-ge…
cs.CV2025
Interpreting the linear structure of vision-language model embedding spaces
Isabel Papadimitriou, Huangyuan Su, Thomas Fel +2
Vision-language models encode images and text in a joint space, minimizing the distance between corresponding image and text pairs. How are language and images organized in this jo…
cs.AI2025
Data-Efficient Multi-Agent Spatial Planning with LLMs
Huangyuan Su, Aaron Walsman, Daniel Garces +2
In this project, our goal is to determine how to leverage the world-knowledge of pretrained large language models for efficient and robust learning in multiagent decision making. W…