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verl-rl-training

Train LLMs with reinforcement learning algorithms using verl.

6|3|Updated Jan 29, 2026
Search Tags:#LLM#reinforcement learning#RLHF#PPO#GRPO#verl
One-click install
npx skills add https://github.com/jonnabio/ace-framework --skill verl-rl-training-jonnabio
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: verl-rl-training
Source: https://github.com/jonnabio/ace-framework/tree/main/.ace/packs/ai-research/verl
Command: npx skills add https://github.com/jonnabio/ace-framework --skill verl-rl-training-jonnabio

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) and assets (resource) components.

What problem does it solve?

This Skill provides guidance for training large language models (LLMs) using reinforcement learning (RL) with verl, addressing the need for scalable, flexible infrastructure backends and support for various RL algorithms.

Core Features & Use Cases

  • Reinforcement Learning Training: Offers instructions for implementing RL algorithms like RLHF, GRPO, PPO, and others for LLM post-training.
  • Scalable Infrastructure: Supports production-ready RL training at scale with flexible infrastructure backends.
  • Algorithm Support: Provides support for multiple RL algorithms, including PPO, GRPO, RLOO, REINFORCE++, DAPO, and more.
  • Use Case: Ideal for teams looking to scale RL training for LLMs, with examples like training reasoning models on math tasks like GSM8K or MATH.

Quick Start

Train a GRPO model for math reasoning using the verl skill:

python3 -m verl.trainer.main_ppo \
  algorithm.adv_estimator=grpo \
  data.train_files=~/data/gsm8k/train.parquet \
  actor_rollout_ref.model.path=Qwen/Qwen2.5-7B \
  actor_rollout_ref.rollout.n=8 \
  actor_rollout_ref.actor.use_kl_loss=True \
  trainer.n_gpus_per_node=8

Frequently Asked Questions about verl-rl-training

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

FAQPage Schema
How do I train a large language model with reinforcement learning using verl?▼

You can train large language models with reinforcement learning using verl by running the PPO trainer with algorithms like GRPO, configuring data files, model paths, and GPU nodes for scalable LLM post-training.

What RL algorithms are supported for LLM post-training with verl?▼

verl supports multiple RL algorithms for LLM post-training, including PPO, GRPO, RLOO, REINFORCE++, and DAPO, allowing you to choose the appropriate method for your reinforcement learning tasks.

Do I need Ray and vLLM to run RLHF training with verl?▼

Yes, you need Ray and vLLM along with torch and transformers, as verl relies on these libraries to provide scalable infrastructure and flexible backends for production-ready reinforcement learning training.

How do I use GRPO to train a math reasoning model on the GSM8K dataset?▼

You can train a math reasoning model on GSM8K by invoking the verl PPO trainer, setting the advantage estimator to GRPO, specifying the dataset path, and enabling KL loss with multi-GPU support.

Can I scale PPO training for large language models on multiple GPUs?▼

Yes, verl is designed for production-ready RL training at scale, allowing you to configure the number of GPUs per node to distribute PPO and GRPO workloads efficiently across your infrastructure.

What is the best way to implement RLHF for LLMs without writing custom training loops?▼

Using verl provides a structured way to implement RLHF for LLMs without custom loops, offering built-in support for scalable infrastructure backends and algorithms like PPO and GRPO for post-training.

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