Morning Edition · Monday, August 17, 2026Published at 2:18 AM EDT · New York

Researchers Propose Reward-Free Rubrics to Stop Model Judges From Over-Crediting Agents

The paper targets a structural weakness in agent evaluation at scale, where a second language model grades runs because executable environment rewards are too slow or unavailable in deployment.

Almost every large-scale agent evaluation used in production today relies on a shortcut. The most reliable signal, an executable environment reward that directly checks whether the agent completed the task, is expensive, slow, or simply una…