👋 Hi, everyone!
verl is a RL training library initiated by ByteDance Seed team and maintained by the verl community.
verl: Volcano Engine Reinforcement Learning for LLMs
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
- [2026/08] verl-vla
v0.1.0 is released: a unified VLA post-training framework for human-in-the-loop data collection, supervised fine-tuning, and reinforcement learning across simulators, real robots, and distributed cloud-edge resources, built on top of verl.
- [2026/08] VeRL-Tinker is released: keep the Tinker Cookbook loop you know, and run SFT, RL, and distillation on verl-managed GPU workers you control; read the blog here.
- [2026/08] VeRL-Omni
v0.2.0 is released: faster diffusion RL, rebuilt Qwen3-Omni multimodal training (DPO & GSPO), plus LTX-2.3, Qwen-Image-Edit support and more.
- [2026/07] RL-Insight is released: online observability for reinforcement learning training. RL-Insight connects training-side metrics, RL state traces, and service dashboards across distributed rollout and optimization workloads.
- [2026/06] verl-SpeCo is pre-released: a co-training framework for speculative decoding across RL training and inference, keeping draft models aligned during training and reusable for accelerated serving, built on top of verl.
- [2026/05] uni-agent is released: a unified agent framework to build, run, and train LLM agents at scale, built on top of verl.
- [2026/05] VeRL-Omni is pre-released: a unified RL stack for diffusion and omni-modal model post-training built on top of verl. Read the blog post for details.
- [2026/05] verl's zero-mismatch HuggingFace rollout vexact is released: with batch-invariant kernels, shared model definition with FSDP, and out-of-box examples compatible with VeOmni.
- [2026/04] verl's Megatron backend LoRA and router replay support is showcased at PyTorch Conference Europe 2026.
- [2026/03] verl is presented at NVIDIA GTC26: session#1, session#2
- [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 https://github.com/verl-project/verl/pull/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.
more...
- [2025/04] [Seed-Thinking-v1.5](https://github.com/ByteDance-Seed/Seed-Thinking-v1.5/blob/main/seed-thinking-v1.5.pdf) tech report is released! Trained with verl, Seed-Thinking-v1.5 achieves 86.7 on AIME 2024, 55.0 on Codeforces and 77.3 on GPQA, demonstrating excellent reasoning abilities in STEM and coding. Beyond reasoning tasks, the method demonstrates notable generalization across diverse domains.
- [2025/07] verl keynote at [AWS AI Hours Singapore](https://pages.awscloud.com/aws-ai-hours-sg.html#agenda) on 7/8, verl & verl-agent project updates at [Agent for SWE meetup](https://lu.ma/e498qhsi) by LF AI & Data Singapore on 7/11.
- [2025/06] verl team will provide latest project updates at [PyTorch Day China](https://www.lfasiallc.com/pytorch-day-china/) on June 7th. Meet our dev team in Beijing!
- [2025/04] [VAPO](https://arxiv.org/pdf/2504.05118) (value-based augmented PPO) paper covers our latest RL method for reasoning models. Trained from Qwen-32B-base model, VAPO achieves 60.4 on AIME 2024, outperforming DAPO-32B.
- [2025/05] [PF-PPO](https://arxiv.org/abs/2409.06957), accepted to ICML 2025, is now supported in verl! PF-PPO enhances policy learning efficiency and robustness by filtering potentially noisy reward signals and reusing high-quality experiences via a replay buffer.
- [2025/04] We will give a tutorial about latest post-training techniques and programming guide for verl at [ICLR 2025 Expo](https://iclr.cc/virtual/2025/calendar?filter_events=Expo+Talk+Panel&filter_rooms=), [SCI-FM workshop](https://open-foundation-model.github.io/) and [LMSys afterparty](https://lu.ma/d23nyynm). Talk materials available [here](https://github.com/eric-haibin-lin/verl-community/tree/main/iclr25).
- [2025/03] verl v0.3.0.post1 is released! See [release note](https://github.com/verl-project/verl/releases/) for details. It achieves [~1.4x speedup](https://tongyx361.github.io/blogs/posts/verl-intro/#/verl-flexible-and-efficient-rl-for-llms) compared to prev versions.
- [2025/05] verl will be presented at [A2M Shanghai](https://a2m.msup.com.cn/home/?aid=4488&city=shanghai) on 5/16 - 5/17.
- [2025/05] verl will be presented at [GOSIM x PyTorch Day 2025](https://paris2025.gosim.org/). See you in Paris!
- [2025/03] We introduced the programming model of verl at the [vLLM Beijing Meetup](https://mp.weixin.qq.com/s/n77GibL2corAtQHtVEAzfg) and [verl intro and updates](https://github.com/eric-haibin-lin/verl-community/blob/main/slides/verl-lmsys-meetup.pdf) at the [SGLang-LMSYS Org Meetup](https://lu.ma/ntjrr7ig) in Sunnyvale mid-March.
- [2025/03] We will present verl(HybridFlow) at EuroSys 2025. See you in Rotterdam!
- [2025/02] verl v0.2.0.post2 is released!
- [2025/02] We presented verl in the Bytedance/NVIDIA/Anyscale Ray Meetup. See you in San Jose!
- [2025/01] [Doubao-1.5-pro](https://team.doubao.com/zh/special/doubao_1_5_pro) is released with SOTA-level performance on LLM & VLM. The RL scaling preview model is trained using verl, reaching OpenAI O1-level performance on math benchmarks (70.0 pass@1 on AIME).
- [2024/12] verl is presented at Ray Forward 2024. Slides available here
- [2024/12] The team presented Post-training LLMs: From Algorithms to Infrastructure at NeurIPS 2024. Slides and video available.
- [2024/10] verl is presented at Ray Summit. Youtube video available.
- [2024/08] HybridFlow (verl) is accepted to EuroSys 2025.
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: Qwen3.5, Qwen3, 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.
- LLM alignment recipes such as Self-play preference optimization (SPPO)
- Flash attention 2, sequence packing, sequence parallelism via DeepSpeed Ulysses, LoRA, Liger-kernel (
USE_LIGER=1).
- 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
Getting Started
Documentation
Quickstart:
Running a PPO example step-by-step:
Reproducible algorithm baselines:
Algorithm recipes (recipe/):
- Optional workflows and baselines live under
recipe/. Each recipe subdirectory includes a small REQUIRED_VERL.txt file describing the intended verl install: pinned recipes use a tag or fixed git SHA; rolling recipes record an explicit VERL_COMMIT (and related submodule / recipe-folder SHAs) so you can pip install verl@git+…@<sha> without guessing. See recipe/README.md for the full index and links.
For code explanation and advance usage (extension):
Blogs from the community
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
verl 是一个灵活、高效、可用于生产环境的大模型强化学习训练库,由字节跳动 Seed 团队发起,由社区维护。它是 HybridFlow 论文(EuroSys 2025)的开源版本。