Reinforcement learning environments for coding
Train AI to code at an expert human level.
Idler builds reinforcement learning environments that teach AI models to code at expert human levels. We create training environments based on real-world coding scenarios that prepare models for the complex challenges they will face in production.
Idler / Vol.01
Method from real engineering work to a graded world
What they cover the engineering work the environments are built from
Debugging
Reproduce, localize, and fix real bugs in a live repo.
Feature work
Build features across an unfamiliar codebase.
Refactors
Restructure code without breaking what works.
Tests & review
Write tests, read diffs, and catch regressions.
Why Idler real, graded, frontier
Real
Environments from real engineering work, never invented benchmarks. The skill transfers.
Graded
Every step checked against a working result. Dense reward, not just pass or fail.
Frontier
Built for the best models, aimed at the engineering they still get wrong.
Notes method write-ups
Dense rewardWhy step-by-step grading beats pass or fail.Note
Environments under RLWhat a graded world does to a model.Study
Shelf lifeRepresenting a codebase as an environment.Note
About the studio
A small team building the training worlds for coding agents.
Idler works quietly with frontier labs, turning production engineering into reinforcement-learning environments and keeping a neutral record of what models can actually do.
We are hiring environment engineers. hi@idler.ai