# Meta-affiliated team reports model selector cuts AI experiment time

_Sunday, September 6, 2026 at 4:25 PM EDT · AI · Latest · Tier 2 — Notable_

![Meta-affiliated team reports model selector cuts AI experiment time — Primary](https://www.marktechpost.com/wp-content/uploads/2026/09/blog1311-6.png)

A research team from Meta FAIR, the University of Oxford and University College London reported AI Research Preference Models that rank unexecuted machine-learning experiments before an agent spends compute running them.

On its AIRS-Bench evaluation, the team said inference-only and agentic variants reached a no-selector baseline's final score in 14.88 and 15.50 hours, respectively, versus 24 hours. The scaffold and benchmark are open source, while the reported system uses a frozen Qwen3.6-27B backbone. The results are reported by the authors and were measured on 20 public text and tabular tasks using a single H200 per task.

## Sources

- [marktechpost.com](https://www.marktechpost.com/2026/09/06/meta-fair-introduces-ai-research-preference-models-rpms-ranking-ml-experiments-before-spending-gpu-hours/)

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Retrieved: 2026-09-06T23:33:02.323Z
Publisher: Tech & Business (techandbusiness.org)