RE-Bench: Evaluating Frontier AI R&D Capabilities of Language Model Agents against Human Experts

Frontier AI safety policies highlight automation of AI research and development (R&D) by AI agents as an important capability to anticipate. However, there exist few evaluations for AI R&D capabilities, and none that are highly realistic and have a direct comparison to human performance. We introduce v1), which consists of 7 challenging, openended ML research engineering environments and data from 71 8-hour attempts by 61 distinct human experts. We confirm that our experts make progress in the environments given 8 hours, with 82% of expert attempts achieving a non-zero score and 24% matching or exceeding our strong reference solutions. We compare humans to several public frontier models through best-of-k with varying time budgets and agent designs, and find that the best AI agents achieve a score 4× higher than human experts when both are given a total time budget of 2 hours per environment. However, humans currently display better returns to increasing time budgets, narrowly exceeding the top AI agent scores given an 8-hour budget, and achieving 2× the score of the top AI agent when both are given 32 total hours (across different attempts). Qualitatively, we find that modern AI agents possess significant expertise in many ML topics-e.g. an agent wrote a faster custom Triton kernel than any of our human experts'-and can generate and test solutions over ten times faster than humans, at much lower cost. We open-source the evaluation environments, human expert data, analysis code and agent trajectories to facilitate future research. 1 * Qally's. Work done in collaboration with METR. † Ordered alphabetically. ‡ Redwood Research. Work done while at METR. § Independent. Work done in collaboration with METR. ¶ Harvard University. Work done while at METR. 1 Environments can be found at github.com/METR/ai-rd-tasks and agent trajectories can be found at transcripts.metr.org. Analysis code and anonymized human expert data coming soon.