idler Material / Montessori

An eval is a learning material.

Reinforcement learning environments that train frontier models to expert level, graded against ground truth. The model is the student; the environment is what it grasps or drops.

Environments · verifiable tasks, dense reward, real rollouts ·
Method · from a problem space to evaluating environments
Domains · where we collaborate, in priority order
Safety
Alignment and oversight. Defense evals fit here. The first call on everything.
Defense
High-stakes capability and red-teaming, including weapons-capability red-teaming.
Science
Bio, pharma, clinical-trials automation, and fundamental research.
Commerce
Indexing workflows from real companies. What we are doing now.
Why Idler · the neutral record
Grounded
Environments from real production work, not invented.
Neutral
A record measured the same way for every lab.
Broad
Across the problem space and its sub-spaces.
About · mission and the neutral record
Mission
Train frontier models on environments built from real problem spaces, graded against ground truth.
The neutral record
A corpus measured the same way for every lab.
Team
A small team, working quietly with frontier labs.
Blog · research notes and method write-ups
Shelf Life
Representing a problem space in thirty pages.
Environments under RL
What our environments do to models when applied with RL.
Dense reward
Why step-by-step grading beats pass or fail.
Careers · open roles
Collaborators
Run this process with new people. Priority: Safety, Defense, Science, Commerce.
Environment engineering
Build and scale environments across problem spaces.
Apply
hi@idler.ai
Contact · request access and partnerships
Request access
See the environments and what they measure.
Partnerships
Run the process together on a problem space.
Reach us
hi@idler.ai
Idler Inc. / San Franciscoidler.aihi@idler.ai