3 papers
cs.LG2025
The Complexity Dynamics of Grokking
Branton DeMoss, Silvia Sapora, Jakob Foerster +2
We demonstrate the existence of a complexity phase transition in neural networks by studying the grokking phenomenon, where networks suddenly transition from memorization to genera…
cs.LG2025
DITTO: Offline Imitation Learning with World Models
Branton DeMoss, Paul Duckworth, Jakob Foerster +2
For imitation learning algorithms to scale to real-world challenges, they must handle high-dimensional observations, offline learning, and policy-induced covariate-shift. We propos…
cs.RO2025
LUMOS: Language-Conditioned Imitation Learning with World Models
Iman Nematollahi, Branton DeMoss, Akshay L Chandra +3
We introduce LUMOS, a language-conditioned multi-task imitation learning framework for robotics. LUMOS learns skills by practicing them over many long-horizon rollouts in the laten…