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verl-rl-training

Train large language models with reinforcement learning using verl.

Updated Feb 15, 2026
Search Tags:#reinforcement learning#ray#distributed training#ppo#rlhf#grpo#llm training
One-click install
npx skills add https://github.com/ricable/mcai --skill verl-rl-training-ricable
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: verl-rl-training
Source: https://github.com/ricable/mcai/tree/main/.agents/skills/verl-rl-training
Command: npx skills add https://github.com/ricable/mcai --skill verl-rl-training-ricable

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps teams train large language models with reinforcement learning when they need scalable post-training workflows, reliable distributed execution, and clear guidance for choosing the right RL algorithm and backend.

Core Features & Use Cases

  • Production RL Training: Run PPO, GRPO, RLOO, REINFORCE++, DAPO, and related algorithms across large GPU clusters.
  • Backend Flexibility: Switch between FSDP, FSDP2, Megatron-LM, vLLM, and SGLang depending on model size and infrastructure.
  • Agentic and Multimodal Workflows: Support multi-turn tool calling, reward modeling, LoRA tuning, and vision-language training.
  • Use Case: A research engineer can use this Skill to set up a GRPO training run for math reasoning, monitor rollout stability, and troubleshoot memory or synchronization issues without hunting through scattered documentation.

Quick Start

Ask the Skill to help you configure and launch a verl reinforcement learning training workflow for your model, dataset, backend, and target algorithm.

Frequently Asked Questions about verl-rl-training

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

FAQPage Schema
How do I set up distributed LLM reinforcement learning training with Ray and verl?▼

To set up distributed LLM reinforcement learning, configure Ray orchestration alongside verl to manage backend resources, define reward functions, and synchronize rollout and optimization across large GPU clusters.

What RL algorithms are supported for large language model post-training?▼

Supported RL algorithms for large language model post-training include PPO, GRPO, RLOO, REINFORCE++, and DAPO, enabling scalable optimization across distributed infrastructure for various reasoning and agentic tasks.

Can I use verl for multi-turn tool use and vision-language training?▼

Yes, verl supports multi-turn tool use and vision-language training, allowing you to orchestrate complex agentic workflows and multimodal model optimization alongside standard text-based reinforcement learning.

How do I choose between FSDP, Megatron-LM, vLLM, and SGLang backends for RLHF?▼

Choosing between FSDP, Megatron-LM, vLLM, and SGLang backends for RLHF depends on your specific model size and infrastructure, allowing you to switch execution engines to match distributed training requirements.

Why does my PPO rollout crash with memory or synchronization issues on large GPU clusters?▼

PPO rollout crashes with memory or synchronization issues often stem from misconfigured backend resource management in distributed training, requiring proper Ray orchestration and verl-specific allocation adjustments for stable execution.

Does verl support LoRA tuning and custom reward modeling for RLHF workflows?▼

Yes, verl supports LoRA tuning and configurable custom reward modeling within RLHF workflows, enabling efficient parameter updates and tailored reward functions for specialized reinforcement learning tasks.

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