In order to address this limitation, we propose a novel class of functions that can characterize the loss landscape of modern deep models without requiring extensive over-parametrization and can also include saddle points.

Large language models (LLMs) with billions of parameters demonstrate impressive performance.

Trained on vast corpora of human language, language models demonstrate emergent human-like reasoning abilities.

The field of inverse constrained RL, which seeks to infer constraints from expert data, is a promising step in this direction.