When frontier intelligence can’t wait, there’s Collinear

Tasks are calibrated to current model behavior, grounded in runnable tools and persistent state, and varied across behaviors and task structures. The goal is not task volume; it is useful learning that transfers to held-out work.

We start with an explicit definition of success, then use the strongest verification method each task supports: programmatic checks for objective outcomes, state and trajectory checks for process constraints, and calibrated expert or model-judge rubrics where judgment is unavoidable. Every verifier is tested against successful, failed, and adversarial trajectories to catch false rewards and reward hacking before delivery.

Yes. The same task system can diagnose a gap, generate trajectories and reward for training, and measure improvement on a separate held-out set. We keep training and evaluation boundaries explicit so higher reward is not mistaken for capability gain.

Bring your own model and harness — open- or closed-source, any framework. Collinear is endpoint-agnostic. Off-the-shelf task libraries let teams start from available inventory, while reusable environments and verifiers shorten the path to new capability-specific variants.