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verifiers

Thu, 28 Aug 2025 15:29:47 +0800

willccbb/verifiers

Verifiers

Environments for LLM Reinforcement Learning

Overview

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.

Setup

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

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curl -LsSf https://astral.sh/uv/install.sh | sh
uv init # create a fresh project
source .venv/bin/activate

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

`1`	`uv add verifiers # uv add 'verifiers[dev]' for Jupyter + testing support`

For training on GPUs with vf.GRPOTrainer, do:

`1`	`uv add 'verifiers[all]' && uv pip install flash-attn --no-build-isolation`

To use the latest main branch, do:

`1`	`uv add verifiers @ git+https://github.com/willccbb/verifiers.git`

To use with prime-rl, see here.

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

git clone https://github.com/willccbb/verifiers.git
cd verifiers
uv sync --all-extras && uv pip install flash-attn --no-build-isolation
uv run pre-commit install

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.

Environments

Environments in Verifiers are installable Python modules which can specify dependencies in a pyproject.toml, and which expose a load_environment function for instantiation by downstream applications (e.g. trainers). See environments/ for examples.

To initialize a blank Environment module template, do:

`1`	`vf-init vf-environment-name # -p /path/to/environments (defaults to "./environments")`

To an install an Environment module into your project, do:

`1`	`vf-install vf-environment-name # -p /path/to/environments (defaults to "./environments")`

To install an Environment module from this repo’s environments folder, do:

`1`	`vf-install vf-math-python --from-repo # -b branch_or_commit (defaults to "main")`

Once an Environment module is installed, you can create an instance of the Environment using load_environment, passing any necessary args:

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import verifiers as vf
vf_env = vf.load_environment("vf-environment-name", **env_args)

To run a quick evaluation of your Environment with an API-based model, do:

`1`	`vf-eval vf-environment-name # vf-eval -h for config options; defaults to gpt-4.1-mini, 5 prompts, 3 rollouts for each`

The core elements of Environments in are:

Datasets: a Hugging Face Dataset with a prompt column for inputs, and either answer (str) or info (dict) columns for evaluation
Rollout logic: interactions between models and the environment (e.g. env_response + is_completed for any MultiTurnEnv)
Rubrics: an encapsulation for one or more reward functions
Parsers: optional; an encapsulation for reusable parsing logic

We support both /v1/chat/completions-style and /v1/completions-style inference via OpenAI clients, though we generally recommend /v1/chat/completions-style inference for the vast majority of applications. Both the included GRPOTrainer as well as prime-rl support the full set of SamplingParams exposed by vLLM (via their OpenAI-compatible server interface), and leveraging this will often be the appropriate way to implement rollout strategies requiring finer-grained control, such as interrupting and resuming generations for interleaved tool use, or enforcing reasoning budgets.

The primary constraint we impose on rollout logic is that token sequences must be increasing, i.e. once a token has been added to a model’s context in a rollout, it must remain as the rollout progresses. Note that this causes issues with some popular reasoning models such as the Qwen3 and DeepSeek-R1-Distill series; see Footguns for guidance on adapting these models to support multi-turn rollouts.

SingleTurnEnv

For tasks requiring only a single response from a model for each prompt, you can use SingleTurnEnv directly by specifying a Dataset and a Rubric. Rubrics are sets of reward functions, which can be either sync or async.

from datasets import load_dataset
import verifiers as vf

dataset = load_dataset("my-account/my-dataset", split="train")

def reward_A(prompt, completion, info) -> float:
	# reward fn, e.g. correctness
	...

def reward_B(parser, completion) -> float:
	# auxiliary reward fn, e.g. format
	...

async def metric(completion) -> float:
	# non-reward metric, e.g. proper noun count
	...

rubric = vf.Rubric(funcs=[reward_A, reward_B, metric], weights=[1.0, 0.5, 0.0])

vf_env = SingleTurnEnv(
	dataset=dataset,
	rubric=rubric
)
results = vf_env.evaluate(client=OpenAI(), model="gpt-4.1-mini", num_examples=100, rollouts_per_example=1)
vf_env.make_dataset(results) # HF dataset format

Datasets should be formatted with columns for:

'prompt' (List[ChatMessage]) OR 'question' (str) fields
- ChatMessage = e.g. {'role': 'user', 'content': '...'}
- if question is set instead of prompt, you can also pass system_prompt (str) and/or few_shot (List[ChatMessage])
answer (str) AND/OR info (dict)
task (str): optional, used by EnvGroup and RubricGroup for orchestrating composition of Environments and Rubrics

The following named attributes available for use by reward functions in your Rubric:

prompt: sequence of input messages
completion: sequence of messages generated during rollout by model and Environment
answer: primary answer column, optional if info is used
state: can be modified during rollout to accumulate any metadata (state['responses'] includes full OpenAI response objects by default)
info: auxiliary info needed for reward computation (e.g. test cases), optional if answer is used
task: tag for task type (used by EnvGroup and RubricGroup)
parser: the parser object declared. Note: vf.Parser().get_format_reward_func() is a no-op (always 1.0); use vf.ThinkParser or a custom parser if you want a real format adherence reward.

For tasks involving LLM judges, you may wish to use vf.JudgeRubric() for managing requests to auxiliary models.

Note on concurrency: environment APIs accept max_concurrent to control parallel rollouts. The vf-eval CLI currently exposes --max-concurrent-requests; ensure this maps to your environment’s concurrency as expected.

vf-eval also supports specifying sampling_args as a JSON object, which is sent to the vLLM inference engine:

`1`	`vf-eval vf-environment-name --sampling-args '{"reasoning_effort": "low"}'`

Use vf-eval -s to save outputs as dataset-formatted JSON, and view all locally-saved eval results with vf-tui.

ToolEnv

For many applications involving tool use, you can use ToolEnv to leverage models’ native tool/function-calling capabilities in an agentic loop. Tools can be specified as generic Python functions (with type hints and docstrings), which will then be passed in JSON schema form to each inference request.

import verifiers as vf
vf_env = vf.ToolEnv(
	dataset= ... # HF Dataset with 'prompt'/'question' + 'answer'/'info' columns
	rubric= ... # Rubric object; vf.ToolRubric() can be optionally used for counting tool invocations in each rollout
	tools=[search_tool, read_article_tool, python_tool], # python functions with type hints + docstrings
	max_turns=10
)

In cases where your tools require heavy computational resources, we recommend hosting your tools as standalone servers (e.g. MCP servers) and creating lightweight wrapper functions to pass to ToolEnv. Parallel tool call support is enabled by default.

For training, or self-hosted endpoints, you’ll want to enable auto tool choice in vLLM with the appropriate parser. If your model does not support native tool calling, you may find the XMLParser abstraction useful for rolling your own tool call parsing on top of MultiTurnEnv; see environments/xml_tool_env for an example.

MultiTurnEnv

Both SingleTurnEnv and ToolEnv are instances of MultiTurnEnv, which exposes an interface for writing custom Environment interaction protocols. The two methods you must override are

from typing import Tuple
import verifiers as vf
from verifiers.types import Messages, State
class YourMultiTurnEnv(vf.MultiTurnEnv):
    def __init__(self,
                 dataset: Dataset,
                 rubric: Rubric,
				 max_turns: int,
                 **kwargs):
	
  async def is_completed(self, messages: Messages, state: State, **kwargs) -> bool:
    # return whether or not a rollout is completed

  async def env_response(self, messages: Messages, state: State, **kwargs) -> Tuple[Messages, State]:
    # return new environment message(s) + updated state

If your application requires more fine-grained control than is allowed by MultiTurnEnv, you may want to inherit from the base Environment functionality directly and override the rollout method.

Training

GRPOTrainer

The included trainer (vf.GRPOTrainer) supports running GRPO-style RL training via Accelerate/DeepSpeed, and uses vLLM for inference. It supports both full-parameter finetuning, and is optimized for efficiently training dense transformer models on 2-16 GPUs.

# install environment
vf-install vf-wordle (-p /path/to/environments | --from-repo)

# quick eval
vf-eval vf-wordle -m (model_name in configs/endpoints.py) -n NUM_EXAMPLES -r ROLLOUTS_PER_EXAMPLE

# inference (shell 0)
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5 vf-vllm --model willcb/Qwen3-1.7B-Wordle \
    --data-parallel-size 7 --enforce-eager --disable-log-requests

# training (shell 1)
CUDA_VISIBLE_DEVICES=6,7 accelerate launch --num-processes 2 \
    --config-file configs/zero3.yaml examples/grpo/train_wordle.py --size 1.7B

Alternatively, you can train environments with the external prime-rl project (FSDP-first orchestration). See the prime-rl README for installation and examples. For example:

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# orchestrator config (prime-rl)
[environment]
id = "vf-math-python"  # or your environment ID

# run (prime-rl)
uv run rl \
  --trainer @ configs/your_exp/train.toml \
  --orchestrator @ configs/your_exp/orch.toml \
  --inference @ configs/your_exp/infer.toml

Troubleshooting

Ensure your wandb and huggingface-cli logins are set up (or set report_to=None in training_args). You should also have something set as your OPENAI_API_KEY in your environment (can be a dummy key for vLLM).
If using high max concurrency, increase the number of allowed open sockets (e.g. ulimit -n 4096)
On some setups, inter-GPU communication can hang or crash during vLLM weight syncing. This can usually be alleviated by setting (or unsetting) NCCL_P2P_DISABLE=1 in your environment (or potentially NCCL_CUMEM_ENABLE=1). Try this as your first step if you experience NCCL-related issues.
If problems persist, please open an issue.

Resource Requirements

GRPOTrainer is optimized for setups with at least 2 GPUs, scaling up to multiple nodes. 2-GPU setups with sufficient memory to enable small-scale experimentation can be rented for <$1/hr.

PRIME-RL

If you do not require LoRA support, you may want to use the prime-rl trainer, which natively supports Environments created using verifiers, is more optimized for performance and scalability via FSDP, includes a broader set of configuration options and user experience features, and has more battle-tested defaults. Both trainers support asynchronous rollouts, and use a one-step off-policy delay by default for overlapping training and inference. See the prime-rl docs for usage instructions.

Further Documentation

See the full docs for more information.

Contributions

Verifiers warmly welcomes community contributions! Please open an issue or PR if you encounter bugs or other pain points during your development, or start a discussion for more open-ended questions.

Please note that the core verifiers/ library is intended to be a relatively lightweight set of reusable components rather than an exhaustive catalog of RL environments. For applications of verifiers (e.g. “an Environment for XYZ task”), you are welcome to submit a PR for a self-contained module that lives within environments/ if it serves as a canonical example of a new pattern. Stay tuned for more info shortly about our plans for supporting community Environment contributions 🙂

Citation

If you use this code in your research, please cite:

@misc{brown_verifiers_2025,
  author       = {William Brown},
  title        = {{Verifiers}: Reinforcement Learning with LLMs in Verifiable Environments},
  howpublished = {\url{https://github.com/willccbb/verifiers}},
  note         = {Commit abcdefg • accessed DD Mon YYYY},
  year         = {2025}
}

Roadmap

A community Environments hub for crowdsourcing, sharing, and discovering new RL environments built with verifiers
Default patterns for hosted resources such as code sandboxes, auxiliary models, and MCP servers
Multimodal input support
Non-increasing token sequences via REINFORCE

ART

Thu, 28 Aug 2025 15:29:23 +0800

OpenPipe/ART

Agent Reinforcement Trainer

Train multi-step agents for real-world tasks using GRPO.

📏 RULER: Zero-Shot Agent Rewards

RULER (Relative Universal LLM-Elicited Rewards) eliminates the need for hand-crafted reward functions by using an LLM-as-judge to automatically score agent trajectories. Simply define your task in the system prompt, and RULER handles the rest—no labeled data, expert feedback, or reward engineering required.

✨ Key Benefits:

2-3x faster development - Skip reward function engineering entirely
General-purpose - Works across any task without modification
Strong performance - Matches or exceeds hand-crafted rewards in 3/4 benchmarks
Easy integration - Drop-in replacement for manual reward functions

# Before: Hours of reward engineering
def complex_reward_function(trajectory):
    # 50+ lines of careful scoring logic...
    pass

# After: One line with RULER
judged_group = await ruler_score_group(group, "openai/o3")

📖 Learn more about RULER →

ART Overview

ART is an open-source RL framework that improves agent reliability by allowing LLMs to learn from experience. ART provides an ergonomic harness for integrating GRPO into any python application. For a quick hands-on introduction, run one of the notebooks below. When you’re ready to learn more, check out the docs.

📒 Notebooks

Agent Task	Example Notebook	Description	Comparative Performance
ART•E LangGraph	🏋️ Train agent	Qwen 2.5 7B learns to search emails using LangGraph	[Link coming soon]
MCP•RL	🏋️ Train agent	Qwen 2.5 3B masters the NWS MCP server	[Link coming soon]
ART•E [RULER]	🏋️ Train agent	Qwen 2.5 7B learns to search emails using RULER	benchmarks
2048	🏋️ Train agent	Qwen 2.5 3B learns to play 2048	benchmarks
Temporal Clue	🏋️ Train agent	Qwen 2.5 7B learns to solve Temporal Clue	[Link coming soon]
Tic Tac Toe	🏋️ Train agent	Qwen 2.5 3B learns to play Tic Tac Toe	benchmarks
Codenames	🏋️ Train agent	Qwen 2.5 3B learns to play Codenames	benchmarks
AutoRL [RULER]	🏋️ Train agent	Train Qwen 2.5 7B to master any task	[Link coming soon]

📰 ART News

Explore our latest research and updates on building SOTA agents.

🗞️ ART now integrates seamlessly with LangGraph - Train your LangGraph agents with reinforcement learning for smarter multi-step reasoning and improved tool usage.
🗞️ MCP•RL: Teach Your Model to Master Any MCP Server - Automatically train models to effectively use MCP server tools through reinforcement learning.
🗞️ AutoRL: Zero-Data Training for Any Task - Train custom AI models without labeled data using automatic input generation and RULER evaluation.
🗞️ RULER: Easy Mode for RL Rewards is now available for automatic reward generation in reinforcement learning.
🗞️ ART·E: How We Built an Email Research Agent That Beats o3 demonstrates a Qwen 2.5 14B email agent outperforming OpenAI’s o3.
🗞️ ART Trainer: A New RL Trainer for Agents enables easy training of LLM-based agents using GRPO.

📖 See all blog posts →

Why ART?

ART provides convenient wrappers for introducing RL training into existing applications. We abstract the training server into a modular service that your code doesn’t need to interface with.
Train from anywhere. Run the ART client on your laptop and let the ART server kick off an ephemeral GPU-enabled environment, or run on a local GPU.
Integrations with hosted platforms like W&B, Langfuse, and OpenPipe provide flexible observability and simplify debugging.
ART is customizable with intelligent defaults. You can configure training parameters and inference engine configurations to meet specific needs, or take advantage of the defaults, which have been optimized for training efficiency and stability.

Installation

ART agents can be trained from any client machine that runs python. To add to an existing project, run this command:

`1`	`pip install openpipe-art`

🤖 ART•E Agent

Curious about how to use ART for a real-world task? Check out the ART•E Agent blog post, where we detail how we trained Qwen 2.5 14B to beat o3 at email retrieval!

🔁 Training Loop Overview

ART’s functionality is divided into a client and a server. The OpenAI-compatible client is responsible for interfacing between ART and your codebase. Using the client, you can pass messages and get completions from your LLM as it improves. The server runs independently on any machine with a GPU. It abstracts away the complexity of the inference and training portions of the RL loop while allowing for some custom configuration. An outline of the training loop is shown below:

Inference
1. Your code uses the ART client to perform an agentic workflow (usually executing several rollouts in parallel to gather data faster).
2. Completion requests are routed to the ART server, which runs the model’s latest LoRA in vLLM.
3. As the agent executes, each system, user, and assistant message is stored in a Trajectory.
4. When a rollout finishes, your code assigns a reward to its Trajectory, indicating the performance of the LLM.
Training
1. When each rollout has finished, Trajectories are grouped and sent to the server. Inference is blocked while training executes.
2. The server trains your model using GRPO, initializing from the latest checkpoint (or an empty LoRA on the first iteration).
3. The server saves the newly trained LoRA to a local directory and loads it into vLLM.
4. Inference is unblocked and the loop resumes at step 1.

This training loop runs until a specified number of inference and training iterations have completed.

🧩 Supported Models

ART should work with most vLLM/HuggingFace-transformers compatible causal language models, or at least the ones supported by Unsloth. Gemma 3 does not appear to be supported for the time being. If any other model isn’t working for you, please let us know on Discord or open an issue on GitHub!

🤝 Contributing

ART is in active development, and contributions are most welcome! Please see the CONTRIBUTING.md file for more information.

📖 Citation

@misc{hilton2025art,
  author = {Brad Hilton and Kyle Corbitt and David Corbitt and Saumya Gandhi and Angky William and Bohdan Kovalenskyi and Andie Jones},
  title = {ART: Agent Reinforcement Trainer},
  year = {2025},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/openpipe/art}}
}

⚖️ License

This repository’s source code is available under the Apache-2.0 License.

🙏 Credits

ART stands on the shoulders of giants. While we owe many of the ideas and early experiments that led to ART’s development to the open source RL community at large, we’re especially grateful to the authors of the following projects:

Finally, thank you to our partners who’ve helped us test ART in the wild! We’re excited to see what you all build with it.