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verl

Coordinate scalable RLHF training for large language models with PPO, GRPO, DAPO, or RLOO.

Updated Aug 27, 2026
Search Tags:#ppo#vllm#fsdp#llm-training#rlhf#grpo#megatron-lm
One-click install
npx skills add https://github.com/tylertitsworth/skills --skill verl-tylertitsworth
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: verl
Source: https://github.com/tylertitsworth/skills/tree/main/verl
Command: npx skills add https://github.com/tylertitsworth/skills --skill verl-tylertitsworth

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Train RLHF LLMs at scale by coordinating rollout generation, policy updates, and reward signals in a unified framework.

Core Features & Use Cases

  • Supports PPO, GRPO, DAPO, RLOO, and REINFORCE++ with stable training loops.
  • Integrates vLLM/SGLang for fast rollout generation and FSDP or Megatron-LM for scalable training.
  • Includes an SFT trainer and end-to-end RLHF pipelines for production-grade experiments.
  • Provides multi-GPU scaling, reward-model integration, checkpointing, and monitoring via WandB.

Quick Start

Configure your training in a YAML file, point verl at your data, and run the trainer to start RLHF fine-tuning of your model.

Frequently Asked Questions about verl

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

FAQPage Schema
How do I scale RLHF training for large language models across multiple GPUs?▼

To scale RLHF, this framework coordinates rollout generation, policy updates, and reward signals using FSDP or Megatron-LM backends across multiple GPUs. You configure multi-GPU infrastructure and YAML schemas to run end-to-end distributed training.

What RL algorithms are supported for fine-tuning LLMs?▼

Supported RL algorithms for LLM fine-tuning include PPO, GRPO, DAPO, RLOO, and REINFORCE++. These operate within stable training loops alongside an SFT trainer for end-to-end production pipelines.

Does vLLM work with FSDP for rollout generation during RLHF?▼

Yes, vLLM works with FSDP for fast rollout generation during RLHF. The framework integrates vLLM or SGLang for inference rollouts and uses FSDP or Megatron-LM for scalable policy updates across multi-GPU setups.

How do I start end-to-end RLHF fine-tuning using a YAML configuration?▼

To start end-to-end RLHF fine-tuning, configure SFT pretraining, RL algorithms, and rollout settings in a YAML file. Point the trainer at your high-quality Parquet data and execute it on multi-GPU infrastructure.

Do I need multi-GPU infrastructure and specific data formats for large-scale RLHF?▼

Yes, large-scale RLHF requires multi-GPU infrastructure and high-quality data in Parquet format. You also need configuration schemas for SFT pretraining, RL algorithms, and rollout settings to execute end-to-end training workflows.

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