veRL

Free

veRL is a flexible and efficient RL framework for LLMs.

FreeFree tier

Type

Open Source

Company

ByteDance

Visit Website GitHub (38 stars)

About veRL

veRL (Volcano Engine Reinforcement Learning for LLMs) is a flexible, efficient, and production-ready RL training library for large language models, initiated by the ByteDance Seed team and maintained by the comunidade. It is the open-source version of the HybridFlow paper, offering a hybrid-controller programming model that enables easy extension of diverse RL algorithms (e.g., GRPO, PPO) with just a few lines of code. veRL seamlessly integrates with existing LLM frameworks like FSDP, Megatron-LM, vLLM, and SGLang, supports flexible device mapping for efficient resource utilization, and provides ready-to-use integrations with popular HuggingFace models. It achieves state-of-the-art throughput via efficient actor model resharding with the 3D-HybridEngine, eliminating memory redundancy and reducing communication overhead during transitions between training and generation phases.

Key Features

Easy extension of diverse RL algorithms via a hybrid-controller programming model (e.g., GRPO, PPO in few lines of code)

Seamless integration with existing LLM infrastructure including FSDP, Megatron-LM, vLLM, and SGLang

Flexible device mapping for efficient resource utilization across different cluster sizes

Ready integration with popular HuggingFace models

State-of-the-art training and inference engine integrations for SOTA RL throughput

Efficient actor model resharding with 3D-HybridEngine to eliminate memory redundancy and reduce communication overhead

Pros & Cons

Pros

Flexible and easy-to-extend algorithm framework
High throughput with state-of-the-art performance
Seamless integration with popular LLM frameworks and models
Open source and production-ready
Efficient memory management via 3D-HybridEngine

Cons

Designed primarily for reinforcement learning post-training, not general-purpose LLM training
Requires significant GPU resources for large-scale experiments

Best For

Reinforcement learning post-training for large language models (LLMs)Implementing RL algorithms like GRPO and PPO for LLM alignmentScalable and efficient LLM fine-tuning with hybrid dataflows

FAQ

What is veRL?

veRL is a flexible, efficient, and production-ready open-source RL training library for LLMs, initiated by the ByteDance Seed team. It supports easy implementation of RL algorithms like GRPO and PPO, and integrates with frameworks such as FSDP and vLLM.

Who maintains veRL?

veRL was initiated by the ByteDance Seed team and is now maintained by the verl community.