PrimeIntellect-ai/verifiers

Environments for LLM Reinforcement Learning

Verifiers is a library of modular components for creating RL environments and training LLM agents. Verifiers includes an async GRPO implementation built around the transformers Trainer, is supported by prime-rl for large-scale FSDP training, and can easily be integrated into any RL framework which exposes an OpenAI-compatible inference client. In addition to RL training, Verifiers can be used directly for building LLM evaluations, creating synthetic data pipelines, and implementing agent harnesses.

Full documentation is available here.

We recommend using verifiers with along uv for dependency management in your own project:

For local (CPU) development and evaluation with API models, do:

For training on GPUs with vf.GRPOTrainer, do:

To use with prime-rl, see here.

To install verifiers from source for core library development, do:

In general, we recommend that you build and train Environments with verifiers, not in verifiers. If you find yourself needing to clone and modify the core library in order to implement key functionality for your project, we'd love for you to open an issue so that we can try and streamline the development experience. Our aim is for verifiers to be a reliable toolkit to build on top of, and to minimize the "fork proliferation" which often pervades the RL infrastructure ecosystem.