Inverse Rubric Optimization: A testbed for agent science

We propose inverse rubric optimization (IRO): tasks where an agent must learn the preferences of a black-box judge under a label budget. IRO tasks induce rich agent behavior and smooth scaling, making them a useful testbed for agent science.