Define the reward
A reward function from checks you can verify: exact answers, tests that pass, schema-valid output.
ML
RL with verifiable
rewards
GRPO post-training on rewards you can verify, inside the same budget and gates.
GRPO with verifiable rewards is the post-training recipe behind most open reasoning models released in 2026: the model samples several answers per prompt and learns from the ones a verifiable check accepts. In Cairn it runs with the same budget, record and gates as every other training run.
The pipeline
Ground it
Captured production scores become the reward function's ground truth, so training targets what users actually needed.
Train on-policy
Sample a group of rollouts per prompt, score them with the reward function and update, inside the budget.
Compare and gate
The same closed-book comparison and regression gate as every other training run.
What the record keeps
The reward definition and its ground truth, the rollouts sampled, the trained adapter, the comparison, and the approver.
Rewards you can verify
Exact answers, passing tests or schema-valid output become the training signal. No reward model to maintain.
Same budget and gates
Rollouts run inside the pipeline budget; the result passes the same comparison and regression gate as any training run.
Grounded in production
Scores captured in production ground the reward function, so the model is trained toward what your users actually needed.
Trainer and evaluation support depend on the installed operators and your deployment.
See how failure can trigger a reviewed improvement cycle →Run it on your own infrastructure.
Apache 2.0 licensed, self-hosted. Talk about what you can verify automatically and we will walk you through it.