What is Lab?

Lab is Prime Intellect’s open research platform for post-training (currently in public beta). 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

Lab 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.

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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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Hosted Training

Hosted Training allows you to run large-scale RL training experiments in your own environments without managing infrastructure. Prime Intellect currently supports agentic RL with LoRA, built on the open-source prime-rl training library.

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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

The platform is powered by the prime-rl training framework. The three key components of the architecture are:

• Trainer — Consumes batches and updates model weights using LoRA
• 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

For Hosted Training, each run is allocated a dedicated Orchestrator which manages environment logic, while Prime Intellect manages multi-tenant LoRA deployments for the Trainer and Inference components. This architecture allows shared hardware across runs, enabling high efficiency and per-token pricing.

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What You Can Do with Lab

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Key Libraries

Lab 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 Access

To get started with Lab:

1. Create a Prime Intellect account
2. Install the Prime CLI: uv tool install prime
3. Authenticate: prime login
4. Set up a workspace: prime lab setup