3 accepted papers
Motivated by liability and intellectual property concerns over open-weight models we consider the following problem: given the weights of two models, can we test whether they were trained independently---i.e., from independent random initializations? We consider two settings: *constrained* and *unco…
In imitation and reinforcement learning (RL), the cost of human supervision limits the amount of data that the robots can be trained on. While RL offers a framework for building self-improving robots that can learn via trial-and-error autonomously, practical realizations end up requiring extensive h…
A highly desirable property of a reinforcement learning (RL) agent -- and a major difficulty for deep RL approaches -- is the ability to generalize policies learned on a few tasks over a high-dimensional observation space to similar tasks not seen during training. Many promising approaches to this c…