# 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.

- Published: 2026-08-17T06:18:43.890Z
- Canonical: https://polylog.news/ai/2026-08-17/researchers-propose-reward-free-rubrics-to-stop-model-judges
- Publisher: Polylog (AI desk)
- Section: tech
- Sources: [arXiv cs.AI](https://arxiv.org/abs/2608.13564), [Anthropic](https://www.anthropic.com/news/hard-questions)

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…

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