README

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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 https://github.com/volcengine/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.

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 https://github.com/volcengine/verl/issues/2388
• DeepSeek 671b optimizations with Megatron https://github.com/volcengine/verl/issues/1033
• Multi-turn rollout and tools using optimizations https://github.com/volcengine/verl/issues/1882
• Agent integration
• Async and off-policy architecture https://github.com/volcengine/verl/pull/2231
• List of breaking changes since v0.4 https://github.com/volcengine/verl/discussions/2270

Getting Started

<a href="https://verl.readthedocs.io/en/latest/index.html"><b>Documentation</b></a>

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 https://github.com/volcengine/verl/pull/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.

Awesome Projects Built with verl

Welcome to register your awesome project build with verl for other developers' reference!

• TinyZero: a reproduction of DeepSeek R1 Zero recipe for reasoning tasks
• SkyThought: RL training for Sky-T1-7B by NovaSky AI team.
• simpleRL-reason: SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild
• Easy-R1: Multi-modal RL training framework
• OpenManus-RL: LLM Agents RL tuning framework for multiple agent environments.
• rllm: async RL training with verl-pipeline
• RAGEN: a general-purpose reasoning agent training framework
• Search-R1: RL with reasoning and searching (tool-call) interleaved LLMs
• ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning
• Skywork-OR1: Skywork open reaonser series
• ToRL: Scaling tool-integrated RL
• Absolute Zero Reasoner: A no human curated data self-play framework for reasoning
• verl-agent: A scalable training framework for long-horizon LLM/VLM agents, along with a new algorithm GiGPO
• RL-Factory: An easy and efficient RL post-training framework for Agentic Learning
• ReTool: ReTool: reinforcement learning for strategic tool use in LLMs. Code release is in progress...
• verl-tool: An unified and easy-to-extend tool-agent training framework based on verl
• PRIME: Process reinforcement through implicit rewards
• MemAgent: MemAgent: Reshaping Long-Context LLM with Multi-Conv RL based Memory Agent
• POLARIS: A Post-training recipe for scaling RL on Advanced Reasoning models
• GUI-R1: GUI-R1: A Generalist R1-style Vision-Language Action Model For GUI Agents
• DeepRetrieval: RL Training of Search Agent with Search/Retrieval Outcome
• Code-R1: Reproducing R1 for Code with Reliable Rewards
• DeepResearcher: Scaling deep research via reinforcement learning in real-world environments
• VAGEN: Training VLM agents with multi-turn reinforcement learning
• RM-R1: RL training of reasoning reward models
• Dr. MAS: Stable end-to-end RL post-training for multi-agent LLM systems
• LUFFY: Learning to Reason under Off-Policy Guidance
• DeepMath: DeepMath-103K data and series models for math reasoning
• PACS: Implicit Actor Critic Coupling via a Supervised Learning Framework for RLVR
• Entropy Mechanism of RL: The Entropy Mechanism of Reinforcement Learning for Large Language Model Reasoning
• LLaSA-TTS-GRPO: TTS fine-tuning with GRPO optimization based on LLASA models
• PF-PPO: Policy Filtration for PPO based on the reliability of reward signals for more efficient and robust RLHF.
• RACRO: Build multi-modal reasoning models via decoupling it into query-conditioned captioning and text-only reasoning
• Agent Lightning: A flexible and extensible framework that enables seamless agent optimization for any existing agent framework.
• VTool-R1: VLMs Learn to Think with Images via Reinforcement Learning on Multimodal Tool Use.
• Kimina-Prover-RL: Training pipeline for formal theorem proving, based on a paradigm inspired by DeepSeek-R1.
• RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization.
• rStar2-Agent: Using reinforcement learning with multi-step tool-calling for math tasks, rStar2-Agent-14B reaches frontier-level math reasoning in just 510 RL training steps
• Vision-SR1: Self-Rewarding Vision-Language Model via Reasoning Decomposition
• SimpleVLA-RL: SimpleVLA-RL: A Simple yet Effective Vision-Language Action Model for Reinforcement Learning
• Table-R1: Table-R1: Inference-Time Scaling for Table Reasoning
• Revisual-R1: Revisual-R1: Advancing Multimodal Reasoning From Optimized Cold Start to Staged Reinforcement Learning
• ARES: ARES: Multimodal Adaptive Reasoning via Difficulty-Aware Token-Level Entropy Shaping
• Meta-Bandit-LLM: Meta-Bandit-LLM: Long-horizon multiturn interactive training for meta-bandit agents
• PokeeResearch: PokeeResearch: State-of-the-art 7B DeepResearch Agent that leverages web search and content reading capabilities to answer complex questions using the most up-to-date information available online.
• Search Self-play: Pushing the Frontier of Agent Capability without Supervision
• OneThinker: All-in-one Reasoning Model for Image and Video
• OpenTinker: Democratizing Agentic Reinforcement Learning as a Service
• FlowRL: Matching reward distributions via flow balance for diverse exploration and generalizable reasoning
• Logic-RL: a reproduction of DeepSeek R1 Zero on 2K Tiny Logic Puzzle Dataset.
• Seed-Coder: RL training of Seed-Coder boosts performance on competitive programming
• all-hands/openhands-lm-32b-v0.1: A strong, open coding agent model, trained with multi-turn fine-tuning
• s3 Efficient Yet Effective Search Agent Training via RL
• Rec-R1: Bridging Generative Large Language Models and Recommendation Systems via Reinforcement Learning
• Explore RL Data Scaling: Exploring Data Scaling Trends and Effects in Reinforcement Learning from Human Feedback
• FIRE: Flaming-hot initiation with regular execution sampling for large language models
• DQO: Enhancing multi-Step reasoning abilities of language models through direct Q-function optimization
• ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models
• cognition-engineering: Test time scaling drives cognition engineering.
• Trust Region Preference Approximation: A simple and stable reinforcement learning algorithm for LLM reasoning.
• AdaRFT: Efficient Reinforcement Finetuning via Adaptive Curriculum Learning
• critic-rl: LLM critics for code generation
• self-rewarding-reasoning-LLM: self-rewarding and correction with generative reward models
• DeepEnlighten: Reproduce R1 with social reasoning tasks and analyze key findings
• MetaSpatial: Reinforcing 3D Spatial Reasoning in VLMs for the Metaverse
• PURE: Credit assignment is the key to successful reinforcement fine-tuning using process reward model
• cognitive-behaviors: Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs
• deepscaler: iterative context scaling with GRPO
• DAPO: the fully open source SOTA RL algorithm that beats DeepSeek-R1-zero-32B
• NoisyRollout: Reinforcing Visual Reasoning with Data Augmentation
• SPEAR: Self-imitation with Progressive Exploration for Agentic Reinforcement Learning (ICLR 2026)
• RuleReasoner: RuleReasoner: Reinforced Rule-based Reasoning via Domain-aware Dynamic Sampling (ICLR 2026)
• MetaphorStar: Image Metaphor Understanding and Reasoning with End-to-End Visual Reinforcement Learning

Contribution Guide

See contributions guide

About ByteDance Seed Team

Founded in 2023, ByteDance Seed Team is dedicated to crafting the industry's most advanced AI foundation models. The team aspires to become a world-class research team and make significant contributions to the advancement of science and society. You can get to know Bytedance Seed better through the following channels👇

We are HIRING! Send us an email if you are interested in internship/FTE opportunities in RL for agents.