Reinforcement learning environments

Environments that train and evaluate AI agents.

Idler builds the reinforcement learning environments that train and evaluate AI agents, grounded in real operations and scored against real outcomes. A neutral record of what models can do, owned by no single lab.

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The corpus · success rate across 18 tests, by areaaverage 0.74
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Method

a skill → a graded test
01PerceivePick a skill and find where models fail at it.
02RepresentTurn it into tasks that each have a clear right answer.
03BuildBuild the test so it cannot be gamed or memorized.
04ScaleMake many versions. The early ones become training data.
05ChooseMeasure how often models succeed, then build the next test around what they miss.
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Domains

most important first

Safety

Keeping AI safe and overseen. The first priority.

Defense

High-stakes work and stress-testing.

Science

Bio, pharma, and research.

Commerce

Real work inside real companies. Live today.

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Why Idler

real, neutral, broad
Real
Built from real decisions, scored against real outcomes. The part you cannot synthesize.
Neutral
A record no single lab owns. Your training signal is not a competitor’s product.
Broad
Any skill. Coding, tool use, judgment, and recovering from mistakes.

Tell us what your models cannot do yet. We build the test to train it.

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