Building an LLM means evaluating it over & over as it changes. Tweak a hyperparameter or scale the model up, & every new checkpoint sends you back through the same benchmarking loop.
We're releasing olmo-eval, a workbench built for this kind of iterative model development. 🧵
olmo-eval builds on our OLMES project, which made benchmark scores comparable & reproducible by standardizing how models are evaluated.
But a final score is only part of the story—olmo-eval works across the intermediate experiments teams compare throughout model development.
Running every benchmark in a locked-down sandbox – as many eval platforms do – is compute-heavy. So olmo-eval instead treats benchmarks differently depending on their runtime needs.
For example, a plain Q&A benchmark runs directly—faster & cheaper than sandboxing.
In olmo-eval, every component is swappable: the model being evaluated, its tools, LLM-as-a-judge graders, & more. You can change one without touching the rest.
Benchmark results land in a uniform schema, so checkpoints stay comparable across a long project.
eval — A repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.
agent harness — The scaffolding around a model that turns it into a working agent — the loop, the tools it can call, and the rules for when to stop.
sandbox — An isolated environment where AI-generated code or agent actions run without being able to touch anything real.
Why it matters
olmo-evalA repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.Full definition → targets the iteration loop rather than one-off scoring: uniform result schemas keep checkpoints comparable across a long project, and lining two checkpoints up question by question shows exactly where a change helped or regressed.
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