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verl-rl-training

Run large-scale RL training for LLMs with verl, GRPO, and FSDP.

3|Updated Feb 21, 2026
Search Tags:#reinforcement learning#distributed training#ppo#grpo#verl#llm training
One-click install
npx skills add https://github.com/ihatesea69/HieuNghi-AI-Skills --skill verl-rl-training-ihatesea69
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: verl-rl-training
Source: https://github.com/ihatesea69/HieuNghi-AI-Skills/tree/main/airesearch_skills/06-post-training/verl
Command: npx skills add https://github.com/ihatesea69/HieuNghi-AI-Skills --skill verl-rl-training-ihatesea69

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill streamlines the complex process of training Large Language Models (LLMs) using reinforcement learning (RL), enabling efficient and scalable post-training for advanced AI capabilities.

Core Features & Use Cases

  • Flexible RL Algorithms: Supports PPO, GRPO, RLOO, and more for diverse training needs.
  • Backend Agnosticism: Seamlessly switch between FSDP, Megatron-LM, and vLLM backends.
  • Production-Ready: Proven to handle models up to 671B parameters with high performance.
  • Use Case: Train a math reasoning LLM using GRPO on the GSM8K dataset with a Qwen2.5-7B model, leveraging a distributed GPU cluster.

Quick Start

Launch GRPO training for math reasoning on GSM8K using the Qwen2.5-7B model.

Frequently Asked Questions about verl-rl-training

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I scale reinforcement learning training for large language models?▼

Scale LLM reinforcement learning training using the verl library to facilitate efficient post-training with flexible backend integrations like FSDP, Megatron-LM, and vLLM.

Can I train a math reasoning LLM using GRPO on the GSM8K dataset?▼

Yes, you can train a math reasoning LLM using GRPO on the GSM8K dataset, such as launching distributed training with a Qwen2.5-7B model on a GPU cluster.

Does verl support multi-turn tool-calling capabilities for agentic workflows?▼

Yes, verl supports multi-turn tool-calling capabilities designed specifically for handling complex agentic workflows during large-scale reinforcement learning training.

What reinforcement learning algorithms are available for LLM post-training?▼

Available reinforcement learning algorithms for LLM post-training include PPO, GRPO, and RLOO, accommodating diverse training requirements for large language models.

Can I use verl to train models with hundreds of billions of parameters?▼

Yes, verl is production-ready and proven to handle large-scale reinforcement learning training for models up to 671B parameters with high performance.

What is the best way to switch between FSDP and Megatron-LM backends for distributed training?▼

Switch between FSDP, Megatron-LM, and vLLM backends seamlessly during LLM reinforcement learning training using the backend agnostic capabilities of the verl library.

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