> ## Documentation Index > Fetch the complete documentation index at: https://mintlify.com/primeintellect-ai/verifiers/llms.txt > Use this file to discover all available pages before exploring further. # Verifiers > Environments for LLM Reinforcement Learning Prime Intellect Prime Intellect ## Welcome to Verifiers Verifiers is a Python library for creating environments to train and evaluate LLMs with reinforcement learning. Build custom RL environments with datasets, reward functions, and interaction harnesses for training capable language model agents. Get up and running in minutes with your first environment Install Verifiers with pip, uv, or poetry View the source code and contribute Browse and share environments with the community ## What are Environments? Environments contain everything required to run and evaluate a model on a particular task: * **Dataset** - Task inputs for the model to solve * **Harness** - Tools, sandboxes, context management, and interaction protocols * **Rubric** - Reward functions to score the model's performance Environments can be used for: * Training models with reinforcement learning (RL) * Evaluating model capabilities * Generating synthetic data * Experimenting with agent harnesses * Creating custom benchmarks ## Key Features From simple single-turn Q\&A to complex multi-turn agents with tool use Define custom rubrics with multiple reward functions and metrics Stateless tools, MCP servers, sandboxed execution, and browser automation Integrated with prime-rl for distributed RL training ## Simple Example Create a basic math environment in just a few lines: ```python theme={null} import verifiers as vf from datasets import Dataset def load_environment(): # Your task data dataset = Dataset.from_list([ {"prompt": [{"role": "user", "content": "What is 2+2?"}], "answer": "4"}, {"prompt": [{"role": "user", "content": "What is 3*5?"}], "answer": "15"}, ]) # Your reward function async def correct_answer(completion, answer) -> float: response = completion[-1]["content"] return 1.0 if answer in response else 0.0 rubric = vf.Rubric(funcs=[correct_answer]) return vf.SingleTurnEnv(dataset=dataset, rubric=rubric) ``` ## Ecosystem Integration Verifiers is tightly integrated with the Prime Intellect platform: * **[Environments Hub](https://app.primeintellect.ai/dashboard/environments?ex_sort=most_stars)** - Share and discover environments * **[prime-rl](https://github.com/PrimeIntellect-ai/prime-rl)** - Distributed training framework * **[Hosted Training](https://app.primeintellect.ai/dashboard/training)** - Cloud training platform * **[Prime Inference](https://docs.primeintellect.ai/inference/overview)** - Model serving for evaluation ## Get Started Install Verifiers and the Prime CLI Build and run your first environment ## Latest Updates **v0.1.9** (January 2026) - New experimental environment classes, monitor rubrics for automatic metric collection, improved workspace setup, better error handling, and documentation overhaul. * **v0.1.9** - Experimental environment classes, monitor rubrics, improved workspace setup * **v0.1.8** - Trajectory-based rollout tracking for token-in token-out training * **v0.1.7** - Improved quickstart, new RLTrainer, documentation improvements ## Citation If you use Verifiers in your research, please cite: ```bibtex theme={null} @misc{brown_verifiers_2025, author = {William Brown}, title = {{Verifiers}: Environments for LLM Reinforcement Learning}, howpublished = {\url{https://github.com/PrimeIntellect-ai/verifiers}}, year = {2025} } ``` Originally created by Will Brown ([@willccbb](https://github.com/willccbb)).