Use case · Bio & science agents
The reward is a measurement.
Agents that drive simulators, lab protocols, and discovery loops — domains where success is read off an instrument.
The shape of the work
Scientific agents are the cleanest RL problem there is: the reward comes from a measurement, not an opinion. The gym wraps your simulators and protocols as environments, trains agents that propose and test, and keeps the full lineage — every hypothesis, every run, every negative result — reproducible and yours.
01
Simulators as environments
Wrap the models you already trust; the loop drives them at scale.
02
Measured reward
Objectives from instruments and assays — no proxy metrics drifting from truth.
03
Negative results kept
Failed experiments are lineage, not garbage. The next loop starts smarter.
04
Private by default
Proprietary data and methods never leave your perimeter.
Work with us
Bring an agent and a goal.
We are taking a small number of design partners. The bar is a real workload, not a logo. Everything runs on your hardware, and you keep the machine.