AI research agents can already propose, implement and score their own machine learning experiments. Idea generation is cheap; verification is not. Training one candidate can consume hours to days of GPU time, so an agent proposes far more candidates than it can afford to run. Which ones get run is the real lever on research progress.
A research team from FAIR at Meta, the University of Oxford and University College London formalizes that lever as research preference and introduces AI Research Preference Models (RPMs). An RPM ranks unexecuted candidates and picks one to execute. It never forecasts an absolute score, the team found language models unreliable at predicting metrics or execution outcomes.
Is it deployable? Partially. RPMs use frozen pretrained LLMs with no fine-tuning, the scaffold AIRA-dojo and benchmark AIRS-Bench are open source, and the backbone Qwen3.6-27B is open weights.
Where the RPM sits in the agent loop
AIRA-dojo is an evolutionary tree search: greedy parent selection, Draft / Improve / Debug operators, highest-validation-score node returned at the end. The RPM intervenes at child creation only. Instead of generating one child and executing it, the agent applies the operator 15 times in parallel to yield 15 unexecuted candidates, then compares them pairwise in a knockout tournament. Only the winner is executed. Each comparison is grounded in context nodes collected by a BFS walk of the explored tree, each shown with the validation score it obtained.
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