What is verl?

What is verl?#

2026-02-26

2 min read time

Applies to Linux

verl is a reinforcement learning training library designed for post-training large language models. It provides a flexible and scalable system for implementing reinforcement learning from human feedback and other reinforcement learning–based optimization workflows. The library is built to integrate with modern language model training and inference stacks while prioritizing performance, modularity, and ease of use.

Features and use cases#

verl provides the following key features:

  • Flexible RL Algorithms: Supports extension and implementation of diverse RL algorithms using a hybrid programming model that unifies single-controller and multi-controller paradigms for efficient data flow execution with minimal code.

  • Modular Integration: Seamlessly integrates with existing LLM infrastructure such as PyTorch FSDP, Megatron-LM, vLLM, SGLang, and Hugging Face models, decoupling computation and data dependencies.

  • Scalable Parallelism: Flexible device mapping and parallelism support allow efficient utilization of multi-GPU and distributed cluster environments.

  • High Performance: Achieves training and rollout throughput through tight integration with optimized engines, and uses techniques such as efficient actor model resharding through the 3D-HybridEngine to reduce memory and communication overhead.

verl is commonly used in the following scenarios:

  • RLHF Training for LLMs: Train language models with RLHF algorithms such as PPO, GRPO, and other recipes for improved alignment and quality.

  • Agent Training: Build and scale RL-based agent training pipelines that interact with environments or tools at scale.

  • Research and Experimentation: Rapidly prototype and evaluate different RL strategies and configurations on large-scale models.

  • Production Deployments: Integrate production-ready RL workflows using diverse backends and distributed computing resources.

Why verl?#

verl is well suited for those who require reinforcement learning for the following reasons:

  • Its hybrid programming model simplifies complex RL dataflow construction while maintaining flexibility for a variety of algorithms.

  • The modular APIs allow easy reuse and extension with existing infrastructure and model ecosystems, reducing engineering overhead.

  • Performance and scalability are core design goals, enabling efficient resource use across GPU clusters and support for multi-node, multi-framework training scenarios.

  • Active community engagement and open-source development make it suitable for both research and production workflows.

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