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PRIME-RL: Async RL Training at Scale

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Overview

PRIME-RL is a framework for large-scale reinforcement learning. It is designed to be easy to use and hackable, yet capable of scaling to 1000+ GPUs. Here is what we think sets it apart:

1. Fully asynchronous RL for high-throughput agentic training at scale.
2. Performant: built to train 1T+ MoE models on 1000+ GPUs with FSDP2 for training and vLLM for inference, with FP8 inference, PD disaggregation, EP and CP parallelism, and more.
3. Native integration with verifiers environments through the Environments Hub, including built-in support for SWE and agentic environments.
4. End-to-end post-training: SFT, RL training, and evals.
5. Multi-node deployment with Slurm and Kubernetes support.
6. Multimodal support for VLMs such as Qwen3-VL.
7. Hackable, modular, and extensible by design.

Models support

The trainer works with both Hugging Face and Prime custom ModelForCausalLM out of the box. For selected families (especially large MoE) we also ship highly optimized training code under src/prime_rl/trainer/models/, including expert parallelism (EP) for MoE layers and context parallelism (CP) for long sequences (see the table), and additional kernels like quack-kernels.

With [model] impl = "auto" (the default), the trainer selects that custom stack when the Hugging Face config type is registered.

Family	Example IDs	MoE	EP	CP
GLM-5 (glm_moe_dsa)	zai-org/GLM-5, zai-org/GLM-5-FP8	yes	✅	❌
Qwen3 MoE (qwen3_moe)	Qwen/Qwen3-30B-A3B, …	yes	✅	✅
Qwen3.5 MoE (qwen3_5_moe)	Qwen/Qwen3.5-35B-A3B, …	yes	✅	✅
Qwen3 / Qwen3.5 VLMs	multimodal.md (qwen3_vl, qwen3_5, qwen3_5_moe)	MoE only on MoE VLMs	MoE only	✅
MiniMax M2 (minimax_m2)	MiniMax/MiniMax-M2	yes	✅	✅
Nemotron H (nemotron_h)	nvidia/Nemotron-3-Nano-30B-A3B, nvidia/Nemotron-3-Super-120B-A12B, …	yes	✅	❌
Trinity (afmoe)	arcee-ai/Trinity-Mini, …	yes	✅	✅
GLM-4 · GLM-4.5 MoE · INTELLECT-3 (glm4_moe)	THUDM/GLM-4-9B-0414, zai-org/GLM-4.5-Air, zai-org/GLM-4.5, PrimeIntellect/INTELLECT-3, …	yes	✅	✅
GPT-OSS (HF, MoE)	openai/gpt-oss-20b, openai/gpt-oss-120b	yes	❌	✅
Other HF causal LMs	Qwen3 dense, Mistral, … (impl = "hf")	varies	❌	✅

Setup

We develop and test on NVIDIA RTX 3090/4090/5090, A100, H100, H200, and B200. If your setup fails, please create an issue.

Prerequisites

Currently, you need at least one NVIDIA GPU to use PRIME-RL. If you don't already have access to one, we recommend our compute platform for everything from renting on-demand single GPUs for developing, debugging and small ablations, to reserving 1000+ GPU clusters for production-scale training.

Quick Setup

Set up PRIME-RL in a single command.

curl -sSL https://raw.githubusercontent.com/PrimeIntellect-ai/prime-rl/main/scripts/install.sh | bash

Manual Setup

1. Clone the repository

git clone https://github.com/PrimeIntellect-ai/prime-rl.git
cd prime-rl

2. Install uv

curl -LsSf https://astral.sh/uv/install.sh | sh
source $HOME/.local/bin/env

3. Install dependencies from the lock file

uv sync --all-extras

3.1. Optional: Install Flash Attention 3 (on Hopper GPUs only, for flash_attention_3 attention backend)

NOTE: This step will take a while, as it builds the Flash Attention 3 extension from source, as it has no wheels prebuilt. NOTE: After this step, you can't run uv sync --all-extras or uv run as it will uninstall the package, you can avoid it by running uv sync --inexact or uv run --no-sync

uv pip install "flash-attn-3 @ git+https://github.com/Dao-AILab/flash-attention.git@main#subdirectory=hopper" --no-build-isolation

Validate your environment setup

1. Check that the environment uses Python 3.12

uv run python -V

2. Check that flash-attn is installed

uv run python -c "import flash_attn"

3. Check that you can run SFT trainer (this requires 1 GPU)

uv run sft @ configs/debug/sft/train.toml

4. Check that you can run the RL trainer (this requires 1 GPU)

uv run trainer @ configs/debug/rl/train.toml

5. Check that you can run the inference server (this requires 1 GPU)

uv run inference @ configs/debug/infer.toml

Keep the inference server running in the background for the next steps.

5.1. Check that you can run the orchestrator against the inference server

uv run orchestrator @ configs/debug/orch.toml

5.2. Check that you can run evals against the inference server

uv run eval @ configs/debug/eval.toml

Additional Setup

1. If you want to log your runs to W&B, log in

uv run wandb login
# Or set `export WANDB_API_KEY=...`

2. If you require gated/ private models or datasets from HuggingFace, log in

uv run hf auth login
# Or set `export HF_TOKEN=...`

Training Examples

We provide end-to-end training examples in the examples directory to highlight features of the framework and guide you through the process of training your own models.

Basic Training: 1 to 8 GPUs

Follow this guide to learn the basics of Prime-RL. You can train your own models on 1 to 8 GPUs. Ideal for getting started and exploring the capabilities of the framework. These guides cover most use cases -- single-turn, multi-turn, tool calling, etc. -- on toy environments and small models.

1. Reverse Text: Train Qwen3-0.6B to reverse a small chunk of text. Demonstrates tiny-scale single-turn SFT and RL training. Can be trained on a single consumer GPU in a few minutes, and is ideal for getting started.
2. Wordle: Train Qwen3-1.7B to play Wordle. A fun example of multi-turn SFT and RL training. Can be trained on a 2-4 H100 GPUs in a few hours. Ideal for exploring the multi-turn training capabilities of the framework.
3. Alphabet Sort: Train Qwen3-4B-Instruct-2507 to sort names alphabetically. Demonstrates multi-turn RL training via LoRA without SFT warmup. Can be trained on a single H100 GPU in just over an hour. Ideal for exploring LoRA-based training.
4. Wiki Search: Train Qwen3-4B-Instruct-2507 to answer trivia questions by searching through a Wikipedia. Demonstrates multi-turn with web search tool use.
5. Hendrycks Sanity: Run a sanity check experiment on DeepSeek-R1-Distill-Qwen-1.5B using a filtered subset of MATH where the model already partially solves 20-80% of problems. Useful for algorithm ablations.

Advanced Training: 32 - 2048 GPUs:

Follow this guide to train large models on hard reasoning and agentic / swe environments. These guides are designed to be run from a Slurm cluster but can also be adapted to k8s deployments.

1. Qwen 3 30B - A3B Math: Train Qwen3-30B-A3B to solve hard math problems.
2. Qwen 3 30B - A3B SWE: Train Qwen3-30B-A3B to solve hard SWE problems.
3. Intellect-3.1: Reproduce our INTELLECT-3.1 training run.
4. MiniMax-M2.5 SWE: Train MiniMax-M2.5 on agentic SWE tasks.
5. High-throughput GLM-5: Train GLM-5 with PD disaggregation and FP8 inference on SWE.

Docs

Check out the docs directory for in-depth guides on how to use PRIME-RL.

• Entrypoints - Overview of the main components (orchestrator, trainer, inference) and how to run SFT, RL, and evals
• Configs - Configuration system using TOML files, CLI arguments, and environment variables
• Environments - Installing and using verifiers environments from the Environments Hub
• Async Training - Understanding asynchronous off-policy training and step semantics
• Logging - Logging with loguru, torchrun, and Weights & Biases
• Checkpointing - Saving and resuming training from checkpoints
• Benchmarking - Performance benchmarking and throughput measurement
• Deployment - Training deployment on single-GPU, multi-GPU, and multi-node clusters
• Memory Usage - Techniques for reducing memory usage (activation checkpointing, offloading, EP, CP, LoRA, etc.)
• Troubleshooting - Common issues and their solutions
• Multimodal - Training VLMs like Qwen3-VL

Contributing

We warmly welcome community contributions! We use issues to track bugs, feature requests, and share our internal roadmap. If you encounter bugs, have pain points during development, or have ideas for new features, please open an issue.

Contributions are welcome via PR. Please follow these guidelines:

1. Install the pre-commit hooks to ensure your code is formatted correctly.
2. Please keep your PR in "Draft" until it is ready for review.
3. If your PR resolves an issue, please link the issue in the PR description
4. If you can, try running the test suite locally to ensure your changes are working as expected.

Pre-Commit Hooks

Please install the pre-commit hooks to ensure your code is formatted correctly.

uv run pre-commit install

Tests

Run the full test suite

uv run pytest -v

To run unit tests, run

uv run pytest tests/unit -v

To run integration tests, run

uv run pytest tests/integration -v

To run CPU-only tests, use the inverse of the gpu marker:

uv run pytest -v -m "not gpu"

License

This project is licensed under the Apache 2.0 license, as found in the License file.

Citation

If you find our work useful, feel free to cite it using

@misc{primeintellect2025prime-rl,
  author = {Prime Intellect},
  title = {PRIME-RL},
  url = {https://github.com/PrimeIntellect-ai/prime-rl},
  year = {2025}
}