Meta FAIR מציגה מודלים RPM למחקר AI

תקציר מקורי באנגליתAI 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 sca