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verl-rl-training

Train large language models with PPO and GRPO using verl.

1|Updated Feb 10, 2026
Search Tags:#reinforcement learning#distributed training#ppo#grpo#verl#llm training
One-click install
npx skills add https://github.com/informatico-madrid/Architect-Expert-Gap-Forge --skill verl-rl-training-informatico-madrid
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: verl-rl-training
Source: https://github.com/informatico-madrid/Architect-Expert-Gap-Forge/tree/main/.github/skills/verl
Command: npx skills add https://github.com/informatico-madrid/Architect-Expert-Gap-Forge --skill verl-rl-training-informatico-madrid

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a robust and scalable framework for training Large Language Models (LLMs) using Reinforcement Learning (RL), addressing the complexities of distributed training and diverse RL algorithms.

Core Features & Use Cases

  • Flexible RL Algorithms: Supports PPO, GRPO, RLOO, REINFORCE++, and more.
  • Backend Agnosticism: Seamlessly switch between FSDP, Megatron-LM, vLLM, and SGLang for rollout and training.
  • Production-Ready: Proven to scale to models with billions of parameters and achieve state-of-the-art performance.
  • Use Case: Train a chatbot to be more helpful and harmless using RLHF, or fine-tune a code generation model to produce more accurate and efficient code snippets.

Quick Start

Use the verl-rl-training skill to launch a GRPO training job for math reasoning using the provided configuration.

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 reinforcement learning training for large language models using the verl library, which supports distributed training backends like FSDP and Megatron-LM. It manages complex distributed setups to enable training for models with billions of parameters.

Can I use PPO and GRPO algorithms for distributed LLM training?▼

Yes, you can use PPO and GRPO algorithms for distributed LLM training. The framework supports multiple RL algorithms including PPO, GRPO, RLOO, and REINFORCE++, allowing flexible algorithm selection for your specific training needs.

Does verl support backend integrations with vLLM and SGLang for rollouts?▼

verl supports backend integrations with vLLM and SGLang for rollouts. It is backend agnostic, allowing you to seamlessly switch between FSDP, Megatron-LM, vLLM, and SGLang to optimize distributed training and rollout efficiency.

What is the best way to fine-tune a code generation model using RLHF?▼

The best way to fine-tune a code generation model using RLHF is through a production-ready RL framework. This Skill enables you to fine-tune models to produce accurate code snippets by applying scalable RL algorithms to your training pipeline.

Are there specific prerequisites for running large-scale RL training with verl?▼

Running large-scale RL training with verl requires an environment configured for distributed training across supported backends like FSDP or Megatron-LM. You need appropriate infrastructure to scale to models with billions of parameters.

Why choose verl-rl-training over other distributed LLM training frameworks?▼

Choose verl-rl-training over other distributed LLM training frameworks for its backend agnosticism and proven scalability. It seamlessly integrates multiple rollout engines like vLLM and SGLang, achieving state-of-the-art performance for billion-parameter models.

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