Verl is an open source, flexible reinforcement learning (RL) training framework specifically designed for post-training large language models (LLMs). Built as an implementation of the HybridFlow architecture, verl offers a user-friendly platform for constructing and executing sophisticated RL dataflows, enabling researchers and practitioners to efficiently extend and experiment with diverse RL algorithms. Its modular APIs decouple computation and data dependencies, allowing seamless integration with existing LLM infrastructures such as PyTorch FSDP, Megatron-LM, and vLLM, as well as with HuggingFace models.
The framework is engineered for performance and scalability, supporting flexible device mapping and parallelism to optimize GPU utilization across various cluster sizes. Verls´s adoption of the 3D-HybridEngine technology ensures efficient actor model resharding, which eliminates memory redundancies and reduces communication overhead during training and generation transitions. The architecture leverages the strengths of both single-controller and multi-controller paradigms, streamlining the execution of complex post-training workflows using a concise and extensible codebase.
Comprehensive documentation and a suite of practical guides—covering installation, backend choices, multi-node training, programming with the HybridFlow model, data preparation, configuration management, and performance tuning—enables rapid onboarding and experimentation. Verls supports community collaboration under Apache License 2.0, with contributions encouraged via GitHub, Slack, or WeChat. The framework adopts modern code quality practices, employing ´ruff´ for linting/formatting and ´pre-commit´ for code management. Continuous integration guidance and open project roadmaps further bolster community engagement, positioning verl as a robust resource for advancing RL-based post-training in cutting-edge Artificial Intelligence systems.
