Project Title

verl — Volcano Engine Reinforcement Learning for Large Language Models (LLMs)

Overview

verl is a flexible, efficient, and production-ready reinforcement learning (RL) training library specifically designed for large language models (LLMs). Developed by ByteDance Seed team and maintained by the verl community, it offers an easy extension of diverse RL algorithms, seamless integration with existing LLM infrastructure, and flexible device mapping. verl stands out for its state-of-the-art throughput and efficient actor model resharding with 3D-HybridEngine.

Key Features

Flexible RL Algorithms Extension: Easily build RL dataflows such as GRPO, PPO with the hybrid-controller programming model.
Seamless Integration with LLM Frameworks: Decouples computation and data dependencies for integration with frameworks like FSDP, Megatron-LM, vLLM, SGLang, etc.
Flexible Device Mapping: Supports various model placements onto different GPUs for efficient resource utilization and scalability.
Ready Integration with HuggingFace Models: Facilitates the use of popular HuggingFace models within the verl framework.

Use Cases

LLM Training and Inference: verl is used for training and inference of large language models, enhancing their performance and efficiency.
Research and Development: verl serves as a platform for researchers and developers to experiment with and develop new RL algorithms for LLMs.
Production Environments: verl's production-readiness makes it suitable for deploying RL-trained LLMs in real-world applications.

Advantages

State-of-the-Art Throughput: verl integrates with SOTA LLM training and inference engines, ensuring high performance.
Efficient Resource Utilization: The flexible device mapping feature allows for efficient use of GPU resources across different cluster sizes.
Community Support: verl is backed by a community that actively contributes to its development and maintenance.

Limitations / Considerations

Complexity for Beginners: The advanced nature of RL and LLMs might pose a steep learning curve for new users.
Hardware Requirements: verl's performance benefits are maximized with access to high-end GPU resources, which might not be available to all users.

HuggingFace Transformers: A library of pre-trained models for Natural Language Processing, which can be integrated with verl for enhanced capabilities.
Megatron-LM: A large, powerful transformer language model that can be used in conjunction with verl for training and deployment.
RLlib: An open-source library for reinforcement learning, offering a different approach to RL training compared to verl.

Basic Information

GitHub: https://github.com/volcengine/verl
Stars: 13,501
License: Unknown
Last Commit: 2025-09-18

📊 Project Information

Project Name: verl
GitHub URL: https://github.com/volcengine/verl
Programming Language: Python
⭐ Stars: 13,501
🍴 Forks: 2,378
📅 Created: 2024-10-31
🔄 Last Updated: 2025-09-18

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