skills.rest
tianhao909
tianhao909COMMUNITY
Explore all skills by this author →
GitHub

verl-rl-training

Train large language models with reinforcement learning using the verl library.

1|Updated Feb 21, 2026
Search Tags:#llm#fine-tuning#reinforcement learning#distributed training#rlhf#verl
One-click install
npx skills add https://github.com/tianhao909/AI-Research-SKILLs-cn --skill verl-rl-training-tianhao909
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: verl-rl-training
Source: https://github.com/tianhao909/AI-Research-SKILLs-cn/tree/main/06-post-training/verl
Command: npx skills add https://github.com/tianhao909/AI-Research-SKILLs-cn --skill verl-rl-training-tianhao909

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 scripts (resource) and 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, addressing the complexities of RLHF and other advanced training paradigms.

Core Features & Use Cases

  • Production-Ready RL Training: Supports training models up to 671B parameters with flexible backend options (FSDP, Megatron-LM, vLLM).
  • Multiple RL Algorithms: Implements PPO, GRPO, DAPO, RLOO, and more for diverse training needs.
  • Use Case: Fine-tune a large language model for a specific domain like medical question answering using Reinforcement Learning from Human Feedback (RLHF) to improve its accuracy and helpfulness.

Quick Start

Use the verl skill to train a Qwen-3 model on the gsm8k dataset using GRPO with 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 fine-tune a large language model using reinforcement learning?▼

You can fine-tune a large language model using reinforcement learning by leveraging the verl framework, which supports algorithms like GRPO and RLHF. It provides scalable distributed training infrastructure to handle large-scale models efficiently.

What reinforcement learning algorithms are supported for LLM training?▼

Supported reinforcement learning algorithms for LLM training include PPO, GRPO, DAPO, and RLOO. These algorithms enable diverse training paradigms, allowing you to apply Reinforcement Learning from Human Feedback to improve model accuracy and helpfulness.

Can I use vLLM for inference during RLHF training?▼

Yes, you can use vLLM for inference during RLHF training. The framework supports flexible backend integrations for distributed training and inference, including vLLM, FSDP, and Megatron-LM, to optimize the reinforcement learning pipeline.

Does distributed RL training support models up to 671B parameters?▼

Yes, distributed RL training supports models up to 671B parameters. The framework integrates with backends like FSDP and Megatron-LM to provide production-ready, scalable infrastructure for training massive large language models.

What dependencies do I need to set up verl for RLHF training?▼

To set up verl for RLHF training, you need verl version 0.3.0 or higher, PyTorch, Ray, vLLM, and Hugging Face Transformers. These dependencies provide the distributed computing and model handling capabilities required for the training pipeline.

Related Skills

Explore More Software Engineering Skills→
Orchestra-ResearchOrchestra-Research
11.5k

openrlhf-training

Train large language models with RLHF algorithms using Ray and vLLM.

Official
Advanced
ScientiaCapitalScientiaCapital
1

model-deployment

Export fine-tuned models to GGUF and deploy with vLLM or Ollama.

Community
Advanced
gztcodegztcode

pytorch-fsdp

Configure PyTorch FSDP for distributed training with parameter tuning.

Community
Advanced
KappTech88KappTech88

fine-tuning-with-trl

Align language models with human preferences using TRL fine-tuning methods.

Community
Advanced