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verl-rl-training

Train LLMs with reinforcement learning at scale using Verl.

Updated Mar 16, 2026
Search Tags:#workflow#llm#backends#distributed-training#rl-training#rlhf#verl
One-click install
npx skills add https://github.com/arsity/scholar-tools --skill verl-rl-training-arsity
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: verl-rl-training
Source: https://github.com/arsity/scholar-tools/tree/main/vendor/ai-research-skills/06-post-training/verl
Command: npx skills add https://github.com/arsity/scholar-tools --skill verl-rl-training-arsity

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Provides comprehensive guidance for production-ready reinforcement learning (RL) training of large language models using Verl, enabling scalable deployment and tooling across diverse backends.

Core Features & Use Cases

  • End-to-end RL training guidance for LLMs with Verl, including setup, configuration, monitoring, and debugging.
  • Supports multiple RL algorithms (PPO, GRPO, RLOO, REINFORCE++, etc.) and backend options (vLLM, Megatron-LM, FSDP, SGLang) for flexible deployment.
  • Use cases include scaling RL training for math reasoning, multi-turn tool use, and alignment workflows across distributed infrastructures.

Quick Start

Run Verl RL training workflow with your base model and selected backend, then monitor metrics and adjust hyperparameters as needed.

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 RLHF training for LLMs across distributed hardware?▼

Scale RLHF training for LLMs by using Verl with a YAML-guided workflow to configure distributed training across multiple backends and hardware setups for production deployment.

What is the best way to configure PPO and GRPO algorithms for LLM training?▼

Configure PPO and GRPO algorithms through Verl's YAML-guided workflow, which manages configuration, rollout, and monitoring across supported backends like vLLM and Megatron-LM.

Does Verl support vLLM and Megatron-LM backends for reinforcement learning?▼

Verl supports vLLM and Megatron-LM backends, alongside FSDP and SGLang, allowing flexible deployment of reinforcement learning algorithms across diverse distributed infrastructures.

Can I use Verl for multi-turn tool use and math reasoning RL workflows?▼

You can use Verl for multi-turn tool use and math reasoning workflows, applying RL algorithms like RLOO and REINFORCE++ to align large language models effectively.

What backends are required to run distributed RL training with Verl?▼

Distributed RL training with Verl requires the Verl runtime and a selected backend such as vLLM, Megatron-LM, or an FSDP-based backend to execute the YAML-guided workflow.

Why should I use a YAML-guided workflow for LLM reinforcement learning training?▼

A YAML-guided workflow streamlines LLM reinforcement learning training by standardizing configuration, rollout, and monitoring, ensuring scalable and manageable deployment across distributed hardware.

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