Reinforced is the tooling layer for reinforcement learning — turning the experimental design of the RL loop into primitives any domain expert can actually use. For the first time, progress isn't gated by compute. It's gated by us.
Compute scaled. Architectures scaled. Data scaled. For the first time — maybe ever — the bottleneck in RL progress is how fast we can design, execute, and learn from each experiment.
A frontier model can learn anything, no matter how complex. Serving it with good infra is like running a data center of geniuses. But those geniuses aren't tenured pharmacokineticists who can intuit which molecular families have clinical efficacy before a trial ever begins.
That intuition is what the expert has. The model can learn it — if we build the harness to teach it. The cost of not having an effective RL flywheel has never been higher. Neither have the gains we can't yet see.
Every breakthrough below is the same move: a domain turned into an objective a model could optimize. Each one was hand-built by specialists, over years. We make that move repeatable — and the next hundred are waiting on the experts who can shape them.
Predicted the 3-D shape of ~200M proteins — a 50-year grand challenge, now largely solved. 2024 Nobel Prize in Chemistry.
Solved 4 of 6 problems at the 2024 International Math Olympiad — silver-medal performance, a first for any machine.
Found matrix-multiply algorithms unbeaten for 50 years; its faster sorting routines now ship in the C++ standard library.
Deep RL sculpted and held plasma inside a real tokamak (DeepMind × EPFL) — shapes physicists had never stabilized this way.
Discovered 2.2M new crystals — ~380k stable. An order of magnitude more than all of recorded science before it.
Superhuman Go, chess, and shogi from self-play alone — the result that proved the loop, and set everything else in motion.
Large-scale RL on chain-of-thought took one general model to gold-medal scores at the 2025 IMO and IOI — no math- or code-specific module.
RL from AI feedback aligns every Claude model to a written constitution — the move that made frontier assistants safe enough to ship.
Pure RL — no human reasoning traces — made self-reflection and verification emerge on their own. Open-weights, and a 2025 Nature cover.
The pattern is everywhere the same — and everywhere bottlenecked by one thing: a specialist who can name the objective. That's the part we can't fake, and the part we're building with you.
We take the experimental-design problem at the heart of RL and turn it into three primitives. Each turn of the flywheel is a step in a user journey — not a YAML file.
Reward, environment, rollout — built from primitives, not config files. The domain expert shapes the signal directly.
Cheap, performant, horizontal. Research-grade rollouts at scale — without standing up a cluster of your own.
Expert eyes confirm what the reward implies. Feed the next iteration. The flywheel turns — faster every loop.
Frontier AI. The deep sciences. Robotics. Or just a sharp opinion. Every one is a seat in the loop.
Reward, rollouts, signal — handled. You iterate on ideas, not infra.
A career of intuition — which molecules show promise, what remission looks like — becomes reward the model climbs.
Reward is physics. We run the rollouts; you shape the signal.
No cluster. No lab. Just tell us how you'd judge the answer — there's a seat in the loop.
We can't guess what a domain expert needs. So we're not going to. We're crowdsourcing the daily workflows, the real tools, the judgment calls — from the people who actually do the work.
Tell us how you would teach the model. That's the differentiator — and it starts with a conversation.
Leave your email and we'll be in touch — or book time directly. We genuinely want to hear how you'd build your loop.