Sign languages are the primary form of communication for millions of deaf individuals.

However, developing models that can process weight-space objects is challenging due to their high dimensional nature.

Generative protein language models are a natural way to design new proteins with desired functions.

In this paper, we propose a novel adaptive preference loss, underpinned by distributionally robust optimization (DRO), designed to address this uncertainty in preference strength.