verl Documentation — Volcano Engine Reinforcement Learning for LLMs
by ByteDance Seed / verl community
Post-train LLMs with PPO, GRPO, and agentic multi-turn RL on a production-grade open-source stack.
Overview
verl is described as a flexible, efficient and production-ready RL training framework designed for large language model post-training, implementing the HybridFlow paper. The documentation walks from installation and single-node PPO training through multinode setup, then into a programming guide that explains the HybridFlow design and how to extend the framework. Algorithm coverage includes Proximal Policy Optimization (PPO), Group Relative Policy Optimization (GRPO), On-Policy RL with Optimal Reward Baseline (OPO), Divergence Proximal Policy Optimization (DPPO), plus recipe variants such as DAPO, SPIN, and SPPO. Advanced sections cover agentic RL training with multi-turn conversations and tool calls, multi-turn rollout, LoRA support for RL algorithms, dataset preparation and custom reward functions, checkpointing, FP8 and NVFP4 quantization, performance tuning and profiling, and monitoring. Hardware support extends beyond NVIDIA to AMD (ROCm) and Ascend (NPU). The project is actively released — the docs' blog section references the verl 0.7 release — making it a current reference for teams doing RLHF, RLVR, or agentic RL in 2026.
At a Glance
- Topic
- Fine-Tuning
- Level
- Advanced
- Format
- Documentation
- Cost
- Free
- Duration
- Self-paced — quickstart in a few hours, full guide ongoing
- Provider
- ByteDance Seed / verl community
- Hands-on
- Yes — code/exercises
- Certificate
- None
What You’ll Learn
- ✓Run your first PPO post-training job on a single node, then scale it multinode
- ✓Choose between PPO, GRPO, OPO, DPPO and recipe variants like DAPO, SPIN, and SPPO
- ✓Train agentic policies with multi-turn rollouts and tool calls
- ✓Prepare RL datasets and implement custom reward functions
- ✓Apply LoRA, FP8/NVFP4 quantization, checkpointing, and profiling to cut training cost
- ✓Extend the framework using the HybridFlow programming model
Highlights
- •Open-source implementation of the HybridFlow RLHF architecture used in production LLM post-training
- •First-class agentic RL: multi-turn conversations with tool calls, not just single-turn preference tuning
- •Runs on NVIDIA, AMD ROCm, and Ascend NPU hardware
- •Actively released and widely used as the base for published RL recipes
Who It’s For
Best For
- ✓ML engineers running RLHF, RLVR, or reasoning-model post-training
- ✓Teams building agentic RL pipelines with tool-using rollouts
- ✓Researchers reproducing or extending GRPO/DAPO-style training recipes
Prerequisites
- •Strong PyTorch and distributed-training experience
- •Understanding of LLM fine-tuning and RLHF concepts
- •Access to multi-GPU hardware
FAQ
What is verl Documentation — Volcano Engine Reinforcement Learning for LLMs?
verl is a flexible, efficient, production-ready reinforcement learning library for LLM post-training, and its documentation is the most complete open guide to running PPO, GRPO, and agentic RL at scale. It is for ML and AI engineers who have outgrown supervised fine-tuning and need real RL training infrastructure.
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: Strong PyTorch and distributed-training experience, Understanding of LLM fine-tuning and RLHF concepts, Access to multi-GPU hardware.
How long does verl Documentation — Volcano Engine Reinforcement Learning for LLMs take?
Expect roughly Self-paced — quickstart in a few hours, full guide ongoing. 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: Run your first PPO post-training job on a single node, then scale it multinode; Choose between PPO, GRPO, OPO, DPPO and recipe variants like DAPO, SPIN, and SPPO; Train agentic policies with multi-turn rollouts and tool calls; Prepare RL datasets and implement custom reward functions; Apply LoRA, FP8/NVFP4 quantization, checkpointing, and profiling to cut training cost; Extend the framework using the HybridFlow programming model.