Can AIs do AI R&D? Reviewing REBench Results with Neev Parikh of METR
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What is REBench and why does it matter for AI research and risk evaluation?
meter, as you said, model of evaluation and threat research. The overall goal is effectively to try and measure like catastrophic risk in a very scientifically rigorous way, have the ability to really like get a handle on the kinds of risks that like AI models are very likely to pose to us, be able to measure that really like accurately, precisely. Do you want your tasks to have this what we call high ceiling, effectively like even at the very top end there's still like a lot of room as much as possible to keep improving your score, right? Like it's somewhat less useful if your task can just be like maxed out at some point and like then there's just no more improvement. Um there was no special prompting or anything, but we're like, huh, that's cheeky.
It wasn't that clever. It was somewhat clever, but not like super clever where like it was like some subtle like you know backdoor or anything like that. It was just like, oh yeah, I will just not train the model. I like change the reference model and just copy it over so like it'll meet all the criteria of the task, but then it'll be like zero training time. There are caveat that there, but it was definitely interesting to see this kind of in the wild and completely unexpected. Well, like we weren't doing anything related to deception or something.
Hello and welcome back to the cognitive revolution. Today my guest is Neve Parrick, member of the technical staff at MEATER, or the model evaluation and threat research organization. MEDA recently released a fascinating new benchmark for evaluating AI systems called Research Engineering Bench, or RE Bench for short, designed to assess how well AI agents can perform real machine learning research engineering tasks. The benchmark consists of seven challenging tasks across three categories optimizing runtimes for performance, minimizing loss functions, and improving model win rates. To succeed, models have to do things like optimize GPU kernels, diagnose and fix corrupt models, and fine tune language models for question answering.
What makes this Eval framework particularly interesting to me is how it approaches the challenges of comparing human and AI performance. Rather than using multiple choice questions or other simply structured problems that might quickly saturate, RE bench tasks are open ended. They require experimental trial and error, and they're scored in such a way that allows for incremental progress with extra effort. The results show that leading models like Cloud 3.5 Sonnet and OpenAIs O one perform somewhere between the tenth and fortieth percentile as compared to professional human machine learning researcher baselines, at least over an eight hour time horizon. Interestingly, extending the AI's time budget by running multiple independent trials and then taking the best result significantly improved AI's relative performance, though still not to the level of top human experts.
Beyond the specific findings, I think this work is worth studying for several big picture reasons. First, it represents a new class of AI evaluation designed to push models out of their comfort zones. It requires reasoning over unfamiliar and in some cases quite unusual problems, effective use of tools, and the ability to maintain coherent plans over an extended period. These tasks simply cannot be solved through simple pattern matching or the regurgitation of training data. Second, the conceptual challenges the meter team faced in creating fair comparisons between humans and AIs highlight just how alien these systems really are. Humans need time to orient themselves to any given task and accomplish little in the first two hours, but are much more able to continue making progress hour after hour.
Whereas by comparison, the AIs make progress almost immediately, but later on tend to get stuck in loops. Third and perhaps most importantly, while current models still lag human experts, we are rapidly approaching capability thresholds that would enable significant automation of AI R and D itself, a scenario which for many years has been thought to signal the beginning of an intelligence explosion.
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Chapters
8 chapters
1
What is REBench and why does it matter for AI research and risk evaluation?
0:00–13:10
2
How does METR design REBench to avoid saturation and keep a high performance ceiling?
13:10–23:28
3
Why does METR give AI agents a time budget and use the “best‑of‑K” strategy?
23:28–33:24
4
What are the main differences between the three task families: runtime, loss, and win‑rate?
33:24–43:39
5
How do humans and AI agents actually perform on the benchmark over 8‑hour, 16‑hour, 32‑hour, and 64‑hour horizons?
43:39–53:35
6
What did the results reveal about Claude 3.5, GPT‑4‑o, and other models compared to expert humans?
53:35–1:01:50
7
What kinds of reward‑hacking or “cheating” behavior were observed, and why does it matter?
1:01:50–1:11:36
8
What are the next steps for METR – new tasks, better elicitation, and how can listeners get involved?
1:11:36–1:41:38
Speakers
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