idler

Environments for frontier models.

Reinforcement learning environments for post-training. Signal moves through the corpus: model to task to grade, and the reward returns to training.

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modeltasks gradereward
The corpus is a living graph.   Environments are nodes · rollouts are the signal moving along the wire.
01Environments

A neutral record, measured the same for every lab.

An environment is a real engineering task with a checkable outcome. The model works it through a tool-using agent loop; every step is scored against ground truth. Not invented problems. Not a reward you cannot verify.

02Method

One process, from a capability to a graded environment.

Perceive
Map a capability and its failure modes until the reward is well defined.
Capability
Represent
Formalize it into a task distribution with a verifiable rubric.
Rubric
Build
Stand up environments that separate cleanly from eval and resist contamination.
No contamination
Scale
Mass-produce variants across the distribution. Early environments become training data.
Distribution
Choose
Score pass@k by model. Point the next environment at what they fail.
pass@k
03Domains

Where the method is pointed. In priority order, by stakes.

Safety
Alignment and oversight. The first call on everything.
Priority
Defense
High-stakes capability and red-team work.
High-stakes
Science
Bio, pharma, research automation.
Research
Commerce
Agentic work on real company operations. Live today.
Live
05Contact

Name the capability your models miss. We build the environment, graded against ground truth.

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