skills.rest
hung-phan
hung-phanCOMMUNITY
Explore all skills by this author →
GitHub

distributed-grpo

Scale GRPO/PPO reinforcement learning training across multiple GPUs with DeepSpeed.

1|Updated Jun 4, 2026
Search Tags:#reinforcement learning#LLM training#DeepSpeed#Ray#distributed learning
One-click install
npx skills add https://github.com/hung-phan/ml-skills --skill distributed-grpo
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: distributed-grpo
Source: https://github.com/hung-phan/ml-skills/tree/main/skills/ml-review/references/ml-training/distributed-grpo
Command: npx skills add https://github.com/hung-phan/ml-skills --skill distributed-grpo

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires openrlhf, verl, trl, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the scaling challenges of GRPO/PPO reinforcement learning for LLMs, allowing training across multiple GPUs, with considerations for reward function design, vLLM generation, DeepSpeed training, and Ray orchestration.

Core Features & Use Cases

  • Multi-GPU Training: Enables training of LLMs on multi-GPU setups, optimizing for performance and scalability.
  • Reward Function Design: Assists in designing custom reward functions for training reinforcement learning models.
  • vLLM Generation: Supports fast completion generation for large models using tensor parallelism and PagedAttention.
  • Use Case: For users looking to train a GRPO/PPO model on a multi-GPU setup for building reasoning models at scale.

Quick Start

Execute the distributed-grpo skill to train a reinforcement learning model with a custom reward function and DeepSpeed on multiple GPUs.

Frequently Asked Questions about distributed-grpo

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

FAQPage Schema
How do I scale GRPO reinforcement learning for LLMs across multiple GPUs?▼

To scale GRPO reinforcement learning across multiple GPUs, you can use distributed training frameworks like OpenRLHF, veRL, or TRL orchestrated with Ray and DeepSpeed. This enables training larger models and handling production workloads beyond single GPU memory limits.

What is the best way to integrate vLLM generation with PPO training?▼

The best way to integrate vLLM generation with PPO training is by using a distributed setup that leverages vLLM's tensor parallelism and PagedAttention. This ensures fast completion generation required for effective reinforcement learning scaling.

Do I need OpenRLHF or veRL to run distributed GRPO training?▼

Yes, you need OpenRLHF, veRL, or TRL as a prerequisite. These frameworks provide the foundational distributed training and generation capabilities required to scale GRPO and PPO workloads across multiple GPUs.

How does Ray orchestration work with DeepSpeed for LLM training?▼

Ray orchestration manages the distributed compute resources, while DeepSpeed handles the actual multi-GPU training execution. Together, they coordinate reward function optimization and model scaling for GRPO and PPO reinforcement learning workflows.

Can I design custom reward functions for distributed PPO models?▼

Yes, you can design and implement custom reward functions for distributed PPO models. The skill assists in creating these functions to optimize reinforcement learning training specifically for multi-GPU DeepSpeed setups.

Why use distributed GRPO instead of single GPU training for reasoning models?▼

Distributed GRPO overcomes single GPU memory and compute bottlenecks, allowing you to train larger reasoning models. It utilizes DeepSpeed and Ray to scale workloads efficiently for production-level reinforcement learning.

Related Skills

Explore More Software Engineering Skills→
ovachieverovachiever
52

openrlhf-training

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

Community
Advanced
ovachieverovachiever
52

serving-llms-vllm

Deploy OpenAI-compatible LLM APIs with vLLM for high-throughput inference.

Community
Advanced
informatico-madridinformatico-madrid
1

openrlhf-training

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

Community
Advanced
Ced3-hanCed3-han

verl-rl-training

Train LLMs with GRPO and PPO reinforcement learning via verl.

Community
Advanced