verl is a flexible, efficient and production-ready RL training library for large language models (LLMs).

verl is the open-source version of HybridFlow: A Flexible and Efficient RLHF Framework paper.

verl is flexible and easy to use with:

• Easy extension of diverse RL algorithms: The hybrid-controller programming model enables flexible representation and efficient execution of complex post-training dataflows. Build RL dataflows such as GRPO, PPO in a few lines of code.

• Seamless integration of existing LLM infra with modular APIs: Decouples computation and data dependencies, enabling seamless integration with existing LLM frameworks, such as FSDP, Megatron-LM, vLLM, SGLang, etc

• Flexible device mapping: Supports various placement of models onto different sets of GPUs for efficient resource utilization and scalability across different cluster sizes.

• Ready integration with popular HuggingFace models

verl is fast with:

• State-of-the-art throughput: SOTA LLM training and inference engine integrations and SOTA RL throughput.

• Efficient actor model resharding with 3D-HybridEngine: Eliminates memory redundancy and significantly reduces communication overhead during transitions between training and generation phases.

News

• [2026/01] verl has been migrated to the verl-project
• [2026/01] verl first meetup was successfully held in Shanghai on 01/10, hosted by Volcengine and NVIDIA, the slides has been uploaded to verl-data.
• [2026/01] The recipe directory has been migrated to a dedicated repository: verl-recipe and added as a submodule. See #4795. It can be used as it was after git submodule update --init --recursive recipe. Note that transfer_queue, fully_async_policy, one_step_off_policy and vla are kept under verl/experimental since they are planned to be merged into the main library. Use them through verl.experimental.{module}.
• [2025/12] Mind Lab successfully used verl and Megatron-bridge to train GRPO Lora for Trillion-parameter model on 64 H800 - See their techblog.
• [2025/10] verl is presented in the PyTorch Conference 2025.
• [2025/08] verl is presented in the PyTorch Expert Exchange Webinar. Slides available.
• [2025/07] The ReTool recipe is fully open sourced. Blog
• [2025/07] The first verl meetup will be held at ICML Vancouver on July 16th! Please join us if you are at ICML! (onsite only)
• [2025/06] verl with Megatron backend enables large MoE models such as DeepSeek-671B and Qwen3-235B.
• [2025/03] DAPO is the open-sourced SOTA RL algorithm that achieves 50 points on AIME 2024 based on the Qwen2.5-32B pre-trained model, surpassing the previous SOTA achieved by DeepSeek's GRPO (DeepSeek-R1-Zero-Qwen-32B). DAPO's training is fully powered by verl and the reproduction code is available in recipe/dapo now.

Key Features

• FSDP, FSDP2 and Megatron-LM for training.
• vLLM, SGLang and HF Transformers for rollout generation.
• Compatible with Hugging Face Transformers and Modelscope Hub: Qwen-3, Qwen-2.5, Llama3.1, Gemma2, DeepSeek-LLM, etc
• Supervised fine-tuning.
• Reinforcement learning with PPO, GRPO, GSPO, ReMax, REINFORCE++, RLOO, PRIME, DAPO, DrGRPO, KL_Cov & Clip_Cov etc.
  • Support model-based reward and function-based reward (verifiable reward) for math, coding, etc
  • Support vision-language models (VLMs) and multi-modal RL with Qwen2.5-vl, Kimi-VL
  • Multi-turn with tool calling
• LLM alignment recipes such as Self-play preference optimization (SPPO)
• Flash attention 2, sequence packing, sequence parallelism support via DeepSpeed Ulysses, LoRA, Liger-kernel.
• Scales up to 671B models and hundreds of GPUs with expert parallelism
• Multi-gpu LoRA RL support to save memory.
• Experiment tracking with wandb, swanlab, mlflow and tensorboard.
• Hardware Support: Supports NVIDIA, AMD, Ascend

Upcoming Features and Changes

• Q3 Roadmap #2388
• DeepSeek 671b optimizations with Megatron #1033
• Multi-turn rollout and tools using optimizations #1882
• Agent integration
• Async and off-policy architecture #2231
• List of breaking changes since v0.4 #2270

Getting Started

Documentation

Quickstart:

• Installation
• Quickstart
• Programming Guide & Tech Talk (in Chinese)
• PPO in verl
• GRPO in verl

Running a PPO example step-by-step:

• Prepare Data for Post-Training
• Implement Reward Function for Dataset
• PPO Example Architecture
• Config Explanation

Reproducible algorithm baselines:

• RL performance on coding, math

For code explanation and advance usage (extension):

• PPO Trainer and Workers

  • PPO Ray Trainer
  • PyTorch FSDP Backend
  • Megatron-LM Backend

• Advanced Usage and Extension

  • Add Models with the FSDP Backend
  • Add Models with the Megatron-LM Backend
  • Multi-turn Rollout Support
  • Search Tool Integration
  • Sandbox Fusion Integration
  • Deployment using Separate GPU Resources
  • Extend to Other RL(HF) algorithms
  • Ray API design tutorial

Blogs from the community

• When Reasoning Models Break Tokenization: The Hidden Complexity of Multiturn Training
• verl deployment on AWS SageMaker
• verl x SGLang Multi-turn Code Walkthrough
• Optimizing SGLang Memory Usage in verl
• SGLang, verl, OpenBMB and Tsinghua University: Pioneering End-to-End Multi-Turn RLHF
• Reinforcement Learning from Human Feedback on AMD GPUs with verl and ROCm Integration
• veMLP x verl ：玩转强化学习训练
• 使用 verl 进行 GRPO 分布式强化学习训练最佳实践
• HybridFlow verl 原文浅析
• 最高提升 20 倍吞吐量！豆包大模型团队发布全新 RLHF 框架，现已开源！

Performance Tuning Guide

The performance is essential for on-policy RL algorithm. We have written a detailed performance tuning guide to help you optimize performance.

Upgrade to vLLM >= v0.8.2

verl now supports vLLM>=0.8.2 when using FSDP as the training backend. Please refer to this document for the installation guide and more information. Please avoid vllm 0.7.x, which contains bugs that may lead to OOMs and unexpected errors.

Use Latest SGLang

SGLang is fully supported with verl, and SGLang RL Group is working extensively on building unique features, including multi-turn agentic RL, VLM RLHF, server-based RL, and partial rollout. Please refer to this document for the installation guide and more information.

Upgrade to FSDP2

verl is fully embracing FSDP2! FSDP2 is recommended by torch distributed team, providing better throughput and memory usage, and is composible with other features (e.g. torch.compile). To enable FSDP2, simply use verl main and set the following options:

actor_rollout_ref.ref.strategy=fsdp2
actor_rollout_ref.actor.strategy=fsdp2
critic.strategy=fsdp2

Furthermore, FSDP2 cpu offloading is compatible with gradient accumulation. You can turn it on to save memory with actor_rollout_ref.actor.fsdp_config.offload_policy=True. For more details, see #1026

AMD Support (ROCm Kernel)

verl now supports FSDP as the training engine (Megatron support coming soon) and both integrates with vLLM and SGLang as inference engines. Please refer to this document for the installation guide and more information, and this document for the vLLM performance tuning for ROCm.

Citation and acknowledgement

If you find the project helpful, please cite:

• HybridFlow: A Flexible and Efficient RLHF Framework
• A Framework for Training Large Language Models for Code Generation via Proximal Policy Optimization

@article{sheng2024hybridflow,
  title   = {HybridFlow: A Flexible and Efficient RLHF Framework},
  author  = {Guangming Sheng and Chi Zhang and Zilingfeng Ye and Xibin Wu and Wang Zhang and Ru Zhang and Yanghua Peng and Haibin Lin and Chuan Wu},
  year    = {2024},
  journal = {arXiv preprint arXiv: 2409.19256}
}

verl is inspired by the design of Nemo-Aligner, Deepspeed-chat and OpenRLHF. The project is adopted and contributed by Bytedance, Anyscale, LMSys.org, Alibaba Qwen team, Shanghai AI Lab, Tsinghua University, UC Berkeley, UCLA, UIUC, University of Hong Kong, ke.com, All Hands AI, ModelBest, JD AI Lab, Microsoft Research, StepFun, Amazon, LinkedIn, Meituan, Camel-AI, OpenManus, Xiaomi, NVIDIA research, Baichuan, RedNote, SwissAI, Moonshot AI (Kimi), Baidu, Snowflake, Skywork.ai, JetBrains, IceSword Lab, and many more.

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Contribution Guide

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