verl Documentation — Volcano Engine Reinforcement Learning for LLMs
by Volcano Engine
Production RL post-training at 671B scale — the HybridFlow implementation the open-weight ecosystem forks.
Overview
verl describes itself as a flexible, efficient and production-ready RL training framework for LLM post-training, and is the open-source implementation of the HybridFlow paper (arXiv:2409.19256). The documentation is arranged as Quickstart, Programming Guide, Data Preparation, Configurations, PPO Examples, Algorithms, Workers, Performance Tuning, Model Integration, Async Training, Low Precision, Advanced Features, Hardware Support, API References, Blog, FAQ and Contributing. The Algorithms section covers PPO, GRPO, DAPO, SPIN, SPPO, OPO, DRO and GPG among others, with the repository additionally listing GSPO, ReMax, REINFORCE++, RLOO, PRIME and Dr.GRPO, plus entropy mechanisms and rollout-correction methods. What distinguishes it architecturally is the decoupling of training backend from rollout engine: training runs on PyTorch FSDP, FSDP2, Megatron-LM, TorchTitan or FSDP-Turbo, while generation runs on vLLM, SGLang, TensorRT-LLM or plain Hugging Face Transformers, with flexible device mapping between the two. The docs reference training runs on DeepSeek 671B and Qwen3-235B, so the scaling guidance is not theoretical. Hardware Support documents NVIDIA plus AMD ROCm on MI300X, MI325X and MI355X across the FSDP, FSDP2 and Megatron backends, with an Ascend tutorial as well. The project is Apache-2.0 with 23.1k stars and 4.4k forks on GitHub, and its README lists over 100 downstream community projects built on it, including TinyZero, Easy-R1, SkyThought and Seed-Coder.
At a Glance
- Topic
- Fine-Tuning
- Level
- Advanced
- Format
- Documentation
- Cost
- Free
- Duration
- ~6-8 hours through quickstart, programming guide and a PPO example, then reference
- Provider
- Volcano Engine
- Hands-on
- Yes — code/exercises
- Certificate
- None
What You’ll Learn
- ✓Understand the HybridFlow programming model that separates control flow from computation
- ✓Configure PPO and GRPO training runs end to end from a data-preparation script
- ✓Pair a training backend such as FSDP or Megatron with a separate rollout engine
- ✓Map devices across actor, critic, reward and rollout workers for throughput
- ✓Tune performance for large models, including low-precision and async training modes
- ✓Integrate a custom Hugging Face model architecture into the training loop
- ✓Run RL training on AMD ROCm or Ascend accelerators, not only NVIDIA
Highlights
- •Reference implementation of a published paper (HybridFlow, arXiv:2409.19256) rather than a blog-driven reimplementation, so the design is inspectable
- •Training backend and rollout engine are independently swappable, so choosing vLLM, SGLang or TensorRT-LLM does not force a trainer rewrite
- •Documented at frontier scale with DeepSeek 671B and Qwen3-235B runs, where most RLHF tutorials stop at 7B
- •First-class AMD ROCm support on MI300X, MI325X and MI355X, which is unusual for RL training stacks
- •23.1k stars, 4.4k forks and 100+ downstream projects including TinyZero, Easy-R1 and SkyThought, so it is an ecosystem rather than a demo repo
Who It’s For
Best For
- ✓ML engineers running RLHF or GRPO training across multiple nodes
- ✓Researchers implementing a new RL algorithm who want existing distributed infrastructure
- ✓Teams post-training open-weight models instead of calling a hosted API
- ✓Anyone needing RL training on AMD ROCm or Ascend hardware
Prerequisites
- •Solid PyTorch and distributed training experience; FSDP or Megatron familiarity is assumed
- •Understanding of PPO or GRPO and the actor-critic-reward-rollout decomposition
- •Access to multi-GPU hardware, since single-consumer-GPU experimentation is not the target
FAQ
What is verl Documentation — Volcano Engine Reinforcement Learning for LLMs?
Official documentation for verl, the open-source implementation of the HybridFlow RLHF architecture and one of the most-used frameworks for RL post-training of LLMs. It covers the hybrid programming model, data preparation, PPO and GRPO examples, worker and rollout configuration, performance tuning, async training and multi-vendor hardware support, at model scales up to 671B parameters.
Is verl Documentation — Volcano Engine Reinforcement Learning for LLMs free?
verl Documentation — Volcano Engine Reinforcement Learning for LLMs is free to access.
What level is verl Documentation — Volcano Engine Reinforcement Learning for LLMs for?
verl Documentation — Volcano Engine Reinforcement Learning for LLMs is aimed at a advanced audience. Recommended background: Solid PyTorch and distributed training experience; FSDP or Megatron familiarity is assumed, Understanding of PPO or GRPO and the actor-critic-reward-rollout decomposition, Access to multi-GPU hardware, since single-consumer-GPU experimentation is not the target.
How long does verl Documentation — Volcano Engine Reinforcement Learning for LLMs take?
Expect roughly ~6-8 hours through quickstart, programming guide and a PPO example, then reference. Most learners work through it at their own pace.
What will I learn from verl Documentation — Volcano Engine Reinforcement Learning for LLMs?
You'll learn: Understand the HybridFlow programming model that separates control flow from computation; Configure PPO and GRPO training runs end to end from a data-preparation script; Pair a training backend such as FSDP or Megatron with a separate rollout engine; Map devices across actor, critic, reward and rollout workers for throughput; Tune performance for large models, including low-precision and async training modes; Integrate a custom Hugging Face model architecture into the training loop; Run RL training on AMD ROCm or Ascend accelerators, not only NVIDIA.
Topics
Sources
This page was written from 2 sources, 1 on domains other than verl.readthedocs.io.