2 papers
cs.LG2025
Ground-Compose-Reinforce: Grounding Language in Agentic Behaviours using Limited Data
Andrew C. Li, Toryn Q. Klassen, Andrew Wang +2
Grounding language in perception and action is a key challenge when building situated agents that can interact with humans, or other agents, via language. In the past, addressing t…
cs.LG2025
Better Training Data Attribution via Better Inverse Hessian-Vector Products
Andrew Wang, Elisa Nguyen, Runshi Yang +3
Training data attribution (TDA) provides insights into which training data is responsible for a learned model behavior. Gradient-based TDA methods such as influence functions and u…