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verl-rl-training

Train LLMs with GRPO and PPO reinforcement learning via verl.

Updated Apr 30, 2026
Search Tags:#distributed#training#reinforcement learning#large language models#verl
One-click install
npx skills add https://github.com/Ced3-han/Harness-Settings --skill verl-rl-training-ced3-han
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: verl-rl-training
Source: https://github.com/Ced3-han/Harness-Settings/tree/main/skills/verl
Command: npx skills add https://github.com/Ced3-han/Harness-Settings --skill verl-rl-training-ced3-han

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires verl, torch, ray, vllm, transformers, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides guidance for training large language models (LLMs) using reinforcement learning (RL) with verl, a flexible and production-ready RL training library.

Core Features & Use Cases

  • RLHF, GRPO, PPO: Implements these RL algorithms for LLM post-training at scale.
  • Flexible Infrastructure: Integrates with various backend options like FSDP, Megatron-LM, vLLM, SGLang.
  • Distributed Training: Scales training up to 671B parameters on GPU clusters.

Quick Start

Use the verl-rl-training skill to perform GRPO training for a math reasoning task with the provided data and model configurations.

Frequently Asked Questions about verl-rl-training

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

FAQPage Schema
How do I train large language models with reinforcement learning using verl?▼

To train large language models with reinforcement learning using verl, you apply algorithms like GRPO and PPO for post-training adjustments. This skill provides scripts and configurations to execute efficient, distributed RL training across GPU clusters.

What reinforcement learning algorithms does verl support for LLM post-training?▼

Verl supports reinforcement learning algorithms like GRPO and PPO for LLM post-training. These algorithms enable scalable adjustments to model behavior, leveraging distributed infrastructure to efficiently process large-scale training tasks.

Can I use verl for distributed training across different backend frameworks?▼

Yes, you can use verl for distributed training across diverse backend frameworks. It integrates with FSDP, Megatron-LM, vLLM, and SGLang, allowing flexible infrastructure choices for scaling reinforcement learning tasks.

How does verl handle scaling reinforcement learning training for very large models?▼

Verl handles scaling reinforcement learning training by leveraging distributed infrastructure capable of handling models up to 671B parameters. It uses Ray and torch to coordinate efficient computation across large GPU clusters.

What dependencies are required to run reinforcement learning training with verl?▼

Required dependencies for verl reinforcement learning training include torch, ray, vllm, and transformers. These frameworks provide the necessary distributed computing, model serving, and neural network foundations for the training process.

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