verl-rl-training

Configure scalable Verl RL training workflows for LLMs across vLLM and Megatron backends.

Updated Mar 18, 2026

Search Tags:#llm #ppo #rl-training #rlhf #grpo #verl

Ready to run

npx skills add https://github.com/tadod12/fraud-detection-research --skill verl-rl-training-tadod12

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: verl-rl-training
Source: https://github.com/tadod12/fraud-detection-research/tree/main/.agent/skills/06-post-training/verl
Command: npx skills add https://github.com/tadod12/fraud-detection-research --skill verl-rl-training-tadod12

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires verl>=0.3.0, torch>=2.0.0, ray>=2.41.0, vllm>=0.8.2, transformers>=4.40.0, and includes references (resource) components.

What problem does it solve?

Training engineers need a guided, scalable path to implement reinforcement learning for LLMs using Verl, covering RLHF, GRPO, PPO, and related post-training workflows.

Core Features & Use Cases

Guidance on end-to-end RL workflows with Verl across backends (vLLM, Megatron, SGLang) and common algorithms.
Real-world scenarios: setting up policy optimization, reward modeling, and distributed training for large models.
Quick-start templates and troubleshooting references for production-grade RL training.

Quick Start

Configure the Verl-based training workflow and run your first RLHF experiment using the included examples.

Frequently Asked Questions about verl-rl-training

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

FAQPage Schema

How do I set up scalable RL training for LLMs with PPO and GRPO?▼

RLHF workflows integrate reward modeling and policy optimization directly into the training pipeline to align large language models. This skill provides references for configuring these post-training workflows across flexible backends like vLLM and Megatron.

Do I need specific PyTorch and Ray versions to run distributed RL training?▼

Yes, distributed RL training requires Verl 0.3.0 or higher, PyTorch 2.0.0 or higher, and Ray 2.41.0 or higher. These version constraints ensure stable integration with backends like vLLM and Megatron for scalable post-training workflows.

Can I use vLLM for RLHF post-training workflows?▼

vLLM is supported as a flexible backend for RLHF post-training workflows alongside Megatron and SGLang. Using vLLM enables efficient inference and generation during policy optimization and reward modeling for large models.

What is the best way to troubleshoot production-grade LLM reinforcement learning?▼

Troubleshooting production-grade LLM reinforcement learning involves using included references to diagnose issues in policy optimization and distributed training. It provides targeted guidance for resolving common configuration and scaling challenges in Verl.

When should I use GRPO instead of PPO for LLM post-training?▼

GRPO and PPO are both supported algorithms for LLM post-training within the same scalable framework. Choosing between them depends on your specific reward modeling and policy optimization requirements for guiding large model training.