Context RL
Reinforcement learning as a service.
The loop that converts captured traces, rubric grades, and expert feedback into measurable improvement: better routing, distilled models you own, and releases that ship only when the evals say they should.
Verified training data
Rubric-passing traces harvested from production, with rejection sampling on grader confidence.
Grader alignment first
Judges are validated against expert consensus before any optimization runs against them.
Distillation to models you own
Open-weight students trained on your verified work, deployed behind Context Inference.
Eval-gated releases
No change ships unless the held-out suite improves. Regressions are structurally impossible to miss.
Context RL is part of the Context platform and works best with Evals. It deploys standalone, and everything it captures joins the same context graph, evals, and audit trail.