Reinforcement learning environments for post-training. Every step is graded against ground truth, and the reward returns to training.
Five steps, strung on one wire. From a loose capability to a graded reward.
Map a capability and its failure modes until the reward is well defined.
Formalize it into a task distribution with a verifiable rubric.
Stand up environments that separate cleanly from eval and resist contamination.
Mass-produce variants across the distribution. Early environments become training data.
Score pass@k by model. Point the next environment at what they fail.
In priority order, by stakes.
Alignment and oversight. The first call on everything.
High-stakes capability and red-team work.
Bio, pharma, research automation.
Agentic work on real company operations. Live today.