Frontier Research

Run large-scale reinforcement learning experiments on open infrastructure, from pretraining to post-training and evaluation.

Open infrastructure for open science

Frontier research shouldn’t be gated by closed clusters. Spin up multi-node training runs in minutes, checkpoint across regions, and reproduce results with environment definitions that live alongside your code.

From supervised fine-tuning to large-scale RL, the same stack — Verifiers, Prime-RL, and Sandboxes — covers the full post-training loop.

Built for reproducibility

Every experiment is declarative: environments, reward functions, and evaluation suites are versioned artifacts you can share, fork, and cite.

FIG. 01 / SETUP

48 h

From zero to a self-improving loop

FIG. 02 / ENVIRONMENTS

2,000+

RL environments ready to compose

FIG. 03 / OVERHEAD

0

Reward engineering required

PLATFORM

Everything this solution needs. None of the overhead.

Environments Hub

Compose repositories, toolchains, and test harnesses into reproducible RL environments — versioned and shareable.

Prime-RL Training

Train across on-demand or reserved clusters, from H200 to B300 — without writing any infrastructure glue.

Sandboxed Rollouts

Every rollout runs isolated, so agents can execute code, browse, and call tools safely at scale.

Verifiable Rewards

Score outcomes against real signals — tests passing, builds compiling, diffs applying cleanly.

READY?

Your model should get smarter while you sleep.

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