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verl-project/verl

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TLDR

Python library for training large language models with reinforcement learning at scale, integrating with existing AI infrastructure like vLLM and Megatron-LM.

Mindmap

mindmap
  root((verl))
    What it does
      RL training for LLMs
      Hybrid controller model
      Memory efficient
    Tech stack
      Python
      PyTorch
      vLLM integration
      Megatron-LM support
    Use cases
      Train billion-param models
      GRPO and PPO algorithms
      Multi-GPU clusters
    Key features
      3D-HybridEngine
      FSDP compatible
      HuggingFace models

Things people build with this

USE CASE 1

Train large language models using reinforcement learning algorithms like GRPO and PPO on multi-GPU clusters.

USE CASE 2

Integrate RL training with existing infrastructure like vLLM for text generation and Megatron-LM for model parallelism.

USE CASE 3

Reduce memory usage and communication overhead when switching between model training and inference phases.

USE CASE 4

Fine-tune HuggingFace models with reinforcement learning at scale up to hundreds of billions of parameters.

Tech stack

PythonPyTorchvLLMMegatron-LMFSDPSGLangHuggingFace

Getting it running

Difficulty · hard Time to first run · 1day+

Requires GPU/CUDA, multiple distributed training frameworks (vLLM, Megatron-LM, FSDP), and careful environment configuration.

Use freely for any purpose including commercial. Keep the notice and disclose changes to the patent grant.

In plain English

verl is an open-source library for reinforcement-learning (RL) post-training of large language models (LLMs). After an LLM has been pretrained on huge piles of text, teams often want to refine it further by rewarding good answers and penalising bad ones, for example, to make a chatbot more helpful, more accurate at maths, or better at using tools. That refinement step is called RL post-training, and verl is a framework for running it efficiently at scale. The project was initiated by the ByteDance Seed team and is the open-source version of the HybridFlow paper. The README highlights two big ideas. First, verl uses a "hybrid-controller" programming model that lets you describe RL training dataflows, algorithms like GRPO and PPO, in a few lines of code, while still allowing complex multi-stage pipelines. Second, it decouples computation from data dependencies through modular APIs, so it plugs into existing LLM infrastructure rather than replacing it. It integrates with training backends such as FSDP and Megatron-LM, inference engines like vLLM and SGLang, and models on HuggingFace. A "3D-HybridEngine" reshards the actor model between training and generation phases to cut memory waste and communication overhead, and the library supports flexible device mapping so you can place models on different GPU sets to suit the cluster you have. You would reach for verl when you are an ML team running RLHF-style or other RL post-training on LLMs, fine-tuning reasoning, code, or tool-use behaviour, and you want a library that scales from small experiments to clusters training very large models. verl is written in Python. The full README is longer than what was provided.

Copy-paste prompts

Prompt 1
How do I set up verl to train a language model with PPO on multiple GPUs using my existing vLLM infrastructure?
Prompt 2
Show me how to use verl's 3D-HybridEngine to reduce memory overhead when alternating between training and generation.
Prompt 3
What are the key differences between GRPO and PPO in verl, and when should I use each one?
Prompt 4
How do I integrate a HuggingFace model into verl and start reinforcement learning training?
Prompt 5
Explain the hybrid-controller programming model in verl and how it separates computation from data dependencies.
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