AI Research Preference Models
AI research agents (AIRA) can now carry machine learning experiments from proposal through implementation and evaluation. Yet progress on frontier tasks is throttled by the cost of evaluations that can consume days of GPU time. When an agent can propose far more candidates than it can afford to run, progress depends on its research preference: how it allocates a fixed execution budget across many candidates. We introduce AI Research Preference Models (RPMs) that predict which candidate solution is most promising, without paying the cost of running them all. We build RPMs from frozen pretrained language models in two variants: an inference-only model that reasons over candidate plans, code...
| Authors | Thomas Simon Foster, Bassel Al Omari, Tingchen Fu et al. |
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| Primary category | cs.AI |
| Published | 14 August 2026 |
| Last updated at source | 25 August 2026 |
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