Reinforcement learning environments

Train AI to code at an expert human level.

Idler builds reinforcement learning environments from real engineering work, each graded against a working result. Models train on the problems they meet in production, not invented benchmarks.

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

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.

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.

Why Idler

real, broad, frontier

Real

Built from real work, not made up. The skills carry over.

Broad

Coding, using tools, long tasks, and recovering from mistakes.

Frontier

Built for the best models, on what they cannot do yet.

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

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