verl
verl: Volcano Engine Reinforcement Learning for LLM
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Practical use requires GPU resources and familiarity with RL concepts; many runtime dependencies (transformers, ray, tensorboard) must be installed and properly configured.
- Low install friction with a pure-Python wheel.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 is permissive, allowing commercial use, modification, and distribution with minimal restrictions. Suitable for proprietary projects provided you include the license notice.
last release 2026-08-14 (0 days) · last repo commit 2026-08-14 · 22,957 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 83,044 downloads/mo, #14,104 on PyPI
Alternatives
Verify before relying
pip install verl
import verl
from verl import Actor, Critic
# Configure and run RL training with modular components- Whether the 18 runtime dependencies are all strictly required or if some are optional for specific use cases
- Performance characteristics and throughput claims relative to other RL training frameworks
- Minimum GPU memory and cluster size requirements for practical training runs
- Whether all HuggingFace model architectures are supported or only a subset
What it is and 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.
The 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.
Use it for
- 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
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
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.
Install
verl on PyPI
Before you install
Low install friction with a pure-Python wheel. Active maintenance with recent releases and 22957 GitHub stars. Requires Python 3.10+. Depends on 18 runtime packages including heavy ML libraries (transformers, ray, tensorboard, wandb), so total environment setup will be substantial despite the package itself being lightweight.
Requires Python 3.10 or later. Practical use requires GPU resources and familiarity with RL concepts; many runtime dependencies (transformers, ray, tensorboard) must be installed and properly configured.
License in practice
Apache-2.0 is permissive, allowing commercial use, modification, and distribution with minimal restrictions. Suitable for proprietary projects provided you include the license notice.
Quickstart
pip install verl
import verl
from verl import Actor, Critic
# Configure and run RL training with modular componentsVerify before relying
- Whether the 18 runtime dependencies are all strictly required or if some are optional for specific use cases
- Performance characteristics and throughput claims relative to other RL training frameworks
- Minimum GPU memory and cluster size requirements for practical training runs
- Whether all HuggingFace model architectures are supported or only a subset
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 18 packagesacceleratecodetimingdatasetsdillhydra-corenumpypandaspeftpyarrowpybind11pylatexencraytorchdatatensordicttransformerswandbpackagingtensorboard |
| Maintenance | Actively maintained 0 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 83,044 / month, #14,104 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: verl-0.9.0-py3-none-any.whl
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