{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/11"}],"enrichment":{"capability":"verl is a reinforcement learning training library for large language models that implements flexible RL algorithms like PPO and GRPO with modular APIs for integration into existing LLM frameworks.","skillfed_tags":["llm-training","reinforcement-learning","distributed-computing"],"use_cases":["Train reward-optimized language models using PPO or GRPO algorithms with flexible dataflow composition","Integrate RL post-training into existing LLM infrastructure built on FSDP, Megatron-LM, or vLLM without rewriting core frameworks","Distribute RL training across heterogeneous GPU clusters with flexible device mapping and model resharding","Build custom RL algorithms by extending the hybrid-controller programming model for domain-specific post-training","Monitor and observe distributed RL training workloads with integrated observability for training metrics and state traces"],"what_it_does":"verl is an open-source reinforcement learning training library for large language models, initiated by ByteDance and maintained by the verl community. It provides a flexible programming model for building RL dataflows such as PPO and GRPO with modular APIs that decouple computation and data dependencies, enabling seamless integration with existing LLM infrastructure like FSDP, Megatron-LM, vLLM, and SGLang. The library supports flexible device mapping across different GPU configurations for efficient resource utilization and scalability.\n\nThe package is designed for production use in post-training LLMs with state-of-the-art throughput through efficient actor model resharding and integration with modern LLM training and inference engines. It includes ready integration with popular HuggingFace models and depends on a substantial ecosystem of ML libraries including transformers, ray, tensorboard, wandb, and PyTorch-related packages for distributed training and monitoring.","worth_installing":"Yes, if you are building or fine-tuning large language models with reinforcement learning and have GPU infrastructure available. The library is actively maintained, has no known vulnerabilities, uses a permissive license, and integrates with standard LLM frameworks. Install friction is low, but practical use requires substantial ML dependencies and GPU resources. Not suitable for CPU-only environments or if you need RL training for non-LLM tasks."},"id":"verl","links":{"html":"https://skillfed.io/packages/verl","md":"https://skillfed.io/packages/verl.md","pypi":"https://pypi.org/project/verl/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-14","license_spdx":null,"license_treatment":"permissive","name":"verl","python_support":"supports_current","summary":"verl: Volcano Engine Reinforcement Learning for LLM"},"popularity":{"monthly_downloads":83044,"position":14104,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.9.0"}