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verl-rl-training

Train large language models with RLHF, GRPO, and PPO using verl.

Updated Jan 12, 2026
Search Tags:#reinforcement learning#distributed training#ppo#rlhf#grpo#verl#llm training
One-click install
npx skills add https://github.com/MesferAli/XCircle --skill verl-rl-training-mesferali
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: verl-rl-training
Source: https://github.com/MesferAli/XCircle/tree/main/.claude/skills/verl
Command: npx skills add https://github.com/MesferAli/XCircle --skill verl-rl-training-mesferali

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) at scale, enabling advanced techniques like RLHF, GRPO, and PPO with flexible infrastructure.

Core Features & Use Cases

  • Production-Ready RL Training: Supports training models up to 671B parameters.
  • Flexible Backends: Easily swap between FSDP, Megatron-LM, vLLM, and SGLang.
  • Multiple RL Algorithms: Implements PPO, GRPO, RLOO, REINFORCE++, and more.
  • Use Case: Train a chatbot to follow complex instructions more accurately using GRPO on a math reasoning dataset, leveraging a distributed training setup with vLLM for efficient rollouts.

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 LLM reinforcement learning training using PPO or GRPO?▼

To scale LLM reinforcement learning training with PPO or GRPO, you can use the verl library. It facilitates distributed training by integrating with backends like FSDP and Megatron-LM, supporting models up to 671B parameters.

Does verl support distributed training backends like vLLM and Megatron-LM for RLHF?▼

Yes, verl supports distributed training for RLHF by integrating with flexible backends. You can easily swap between vLLM, SGLang, FSDP, and Megatron-LM to optimize computation and rollout efficiency during large-scale reinforcement learning.

What is the best way to train a chatbot for math reasoning using GRPO on GSM8K?▼

The best way to train a chatbot for math reasoning using GRPO on GSM8K is launching verl with a Qwen2.5-7B model. It leverages a distributed training setup with vLLM for efficient rollouts to improve instruction accuracy.

Can I train a 671B parameter large language model using reinforcement learning?▼

Yes, you can train a 671B parameter large language model using reinforcement learning. verl provides production-ready RL training capabilities that support advanced algorithms like PPO, GRPO, and RLOO for massive scale model optimization.

Which reinforcement learning algorithms are available for large language model training?▼

Available reinforcement learning algorithms for large language model training include PPO, GRPO, RLOO, and REINFORCE++. verl implements these advanced RLHF techniques to streamline complex instruction following and reasoning tasks.

Why use FSDP over Megatron-LM for distributed RL training rollouts?▼

Choosing FSDP over Megatron-LM for distributed RL training rollouts depends on your infrastructure preferences. verl offers flexible backend integrations for both, allowing you to easily swap frameworks to match your specific distributed computation needs.

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