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verl-rl-training

Run large-scale GRPO and PPO training for LLMs with verl.

Updated Jan 30, 2026
Search Tags:#llm#reinforcement learning#distributed training#ppo#rlhf#grpo#post-training
One-click install
npx skills add https://github.com/gagan114662/content_books --skill verl-rl-training-gagan114662
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: verl-rl-training
Source: https://github.com/gagan114662/content_books/tree/main/AI-research-SKILLs/06-post-training/verl
Command: npx skills add https://github.com/gagan114662/content_books --skill verl-rl-training-gagan114662

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a robust framework for training Large Language Models (LLMs) using reinforcement learning (RL) at scale, simplifying complex RLHF, GRPO, and PPO implementations.

Core Features & Use Cases

  • Scalable RL Training: Supports training models up to 671B parameters with flexible backend options (FSDP, Megatron-LM, vLLM).
  • Multiple RL Algorithms: Implements PPO, GRPO, DAPO, and more for diverse post-training needs.
  • Use Case: Train a chatbot to follow complex instructions more accurately using GRPO on a large dataset of user interactions and expert feedback.

Quick Start

Launch GRPO training for math reasoning tasks using the verl skill with Qwen2.5-7B on 8 GPUs.

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 post-training using PPO or GRPO?▼

You can scale LLM reinforcement learning post-training using the verl framework, which implements PPO and GRPO algorithms. It supports distributed training backends like FSDP, Megatron-LM, and vLLM to efficiently fine-tune models for complex reasoning and instruction following.

Can I use Megatron-LM or FSDP for distributed RLHF training?▼

Yes, you can use Megatron-LM or FSDP for distributed RLHF training. The framework provides flexible infrastructure backends including FSDP, Megatron-LM, and vLLM, enabling scalable reinforcement learning post-training for large language models.

What is the best way to train a chatbot for complex instruction following with RLHF?▼

The best way to train a chatbot for complex instruction following is using GRPO or PPO algorithms on a large dataset of user interactions and expert feedback. This framework facilitates large-scale reinforcement learning to improve instruction accuracy.

How do I launch GRPO training for math reasoning tasks on multiple GPUs?▼

To launch GRPO training for math reasoning tasks, you can use the framework with models like Qwen2.5-7B on 8 GPUs. It simplifies complex RL implementations and uses distributed training backends to efficiently scale the reinforcement learning process.

Does verl support training models up to 671B parameters for post-training?▼

Yes, verl supports scalable RL training for models up to 671B parameters. It uses flexible backend options including FSDP, Megatron-LM, and vLLM to handle distributed training for large-scale reinforcement learning post-training.

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