Meta paper uses research preference models to guide AI agents' experiments

Lewis Tunstall praises a new Meta paper on research preference models (RPMs), which instill "research taste" in agents by treating experiments as tree nodes.

An RPM acts as an LLM judge that selects the most promising candidate experiment before it is run, reducing wasted compute.

Tunstall notes the resulting trajectories could train domain-specific RPMs, which would be valuable in hard fields such as the natural sciences.