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A Few Things I Learned About Evals - Ryan Bloom

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I spent most of 2024 working on capability evaluations for AI agents. This post collects some salient takeaways from that work. Although much of what I learned was thanks to my kind and talented colleagues at METR, this post represents my own views only. A basic and general challenge with evaluation—one I expect is familiar to anyone who’s designed a standardized test or managed a work trial—is that an eval is not the real world, but it’s supposed to tell us something about the real world. Even with relatively flexible infrastructure, there are frequent trade-offs between practicality and validity. How do we quickly, cheaply tell if agents can manage long-running tasks on an expensive GPU cluster? What’s a controlled, reproducible way to assess how they deal with messy environments? At a high level, a good place to start is defining a budget and other practical constraints. Then take the overarching question (e.g. “does model X pose a catastrophic risk to society?”) and keep breaking i

I spent most of 2024 working on capability evaluations for AI agents. This post collects some salient takeaways from that work. Although much of what I learned was thanks to my kind and talented colleagues at METR, this post represents my own views only. To design a good eval, set the right constraints A basic and general challenge with evaluation—one I expect is familiar to anyone who's designed a standardized test or managed a work trial—is that an eval is not the real world, but it's supposed to tell us something about the real world. Even with relatively flexible infrastructure, there are

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