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Prime Intellect is an open research platform for post-training. It unifies the Environments Hub, Hosted Training, and Hosted Evaluations into a full-stack platform for reinforcement learning research and optimization. The platform enables the entire lifecycle of post-training research — from large-scale agentic RL, to inference and evaluation — without needing to worry about the costs of massive GPU clusters or the details of low-level algorithm implementation.

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Core Concepts

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Environments

The platform is built around environments, which contain everything needed to run a model on a task:
  • A dataset of tasks (input prompts with optional ground-truth answers)
  • A harness for the model (tools, sandboxes, context management, multi-turn interactions)
  • A rubric to score the model’s performance (reward functions, metrics)
Environments are self-contained Python modules built with the verifiers library. They can be used to train models with reinforcement learning, evaluate capabilities, generate synthetic data, optimize prompts, experiment with agent harnesses, and more. See The Environment Model for a detailed breakdown of environment types and the rollout loop.

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Environments Hub

The Environments Hub is a community registry where you can browse and deploy open-source environments, or create and share your own. Environments support running evaluations with any OpenAI-compatible model endpoint, or training any supported open-weights model via Hosted Training. The hub includes a range of pre-built environments spanning math, coding, games, search and tool use, multimodal tasks, and more — as well as validated implementations of popular benchmarks like AIME, MATH-500, and Humanity’s Last Exam.

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Model Factory

Model Factory is the training side of the platform, built on the open-source prime-rl training library. It covers two things:
  • Hosted Training — submit a config and we run it on GPUs we operate, on-demand and billed per GPU-hour. Full-parameter RL and SFT, no cluster to reserve.
  • Model Factory Clusters — a dedicated cluster running training, inference, Slurm and Kubernetes side by side, for when you need control over the stack.
The earlier shared multi-tenant LoRA service has been retired.

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Hosted Evaluations

Run evaluations against any environment directly from the web UI or CLI, using Prime Inference or your own OpenAI-compatible endpoints. Results are saved and viewable in the terminal UI or on the dashboard.

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Architecture

Training is powered by the prime-rl framework. The three key components are:
  • Trainer — Consumes batches and updates model weights
  • Inference — Serves the model via an OpenAI-compatible API with live weight updating
  • Orchestrator — Manages environment logic, schedules rollout requests, and coordinates the training loop
Each Hosted Training run gets its own trainer, inference, and orchestrator pods, sized by the [deployment] block in your config.

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What You Can Do

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Key Libraries

The platform integrates with two core open-source libraries: verifiers — The environment framework. Provides building blocks for datasets, rubrics, tools, sandboxes, and multi-turn interactions. Environments built with verifiers are portable across local evaluation, hosted evaluation, and Hosted Training. prime-rl — The training framework. Supports fully asynchronous distributed RL at scale with FSDP2 training and vLLM inference. Used both for self-hosted training on your own GPUs and as the backend for Hosted Training.

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Getting Started

  1. Create a Prime Intellect account
  2. Install the Prime CLI: uv tool install prime
  3. Authenticate: prime login
  4. See what you can run: prime train models

Getting Started

Launch your first on-demand Hosted Training run.

End-to-End Training Run

Walk through a complete Hosted Training run.