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MesferAli
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torchforge-rl-training

Guide PyTorch-native agentic RL training with GRPO using torchforge.

Updated Jan 12, 2026
Search Tags:#llm#reinforcement learning#pytorch#agentic ai#distributed training#torchforge
One-click install
npx skills add https://github.com/MesferAli/XCircle --skill torchforge-rl-training-mesferali
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: torchforge-rl-training
Source: https://github.com/MesferAli/XCircle/tree/main/.claude/skills/torchforge
Command: npx skills add https://github.com/MesferAli/XCircle --skill torchforge-rl-training-mesferali

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill simplifies the process of training Reinforcement Learning (RL) agents using PyTorch, abstracting away complex infrastructure concerns like distributed training and model synchronization.

Core Features & Use Cases

  • PyTorch-Native RL: Develop and experiment with RL algorithms directly in PyTorch without external dependencies like Ray.
  • Scalable Training: Leverages Meta's Monarch and TorchTitan for efficient training across multiple GPUs and nodes.
  • Algorithm Isolation: Focus on implementing RL algorithms while the library handles infrastructure.
  • Use Case: Train a language model to follow instructions using GRPO, leveraging distributed training across multiple GPUs with minimal configuration.

Quick Start

Use the torchforge skill to launch GRPO training for Qwen2.5-7B-Instruct on the GSM8K dataset.

Frequently Asked Questions about torchforge-rl-training

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

FAQPage Schema
How do I train reinforcement learning agents in PyTorch without external dependencies like Ray?▼

PyTorch-native reinforcement learning training is simplified by separating infrastructure from algorithms. You can develop and experiment with RL algorithms directly using Meta's torchforge library without relying on external dependencies like Ray.

Does torchforge support distributed RL training across multiple GPUs?▼

Distributed RL training across multiple GPUs is supported through integration with Monarch and TorchTitan. This enables efficient, scalable training across multiple nodes while isolating your algorithm implementation from infrastructure concerns.

What's the best way to fine-tune a language model using GRPO?▼

GRPO training is supported natively for fine-tuning language models to follow instructions. You can launch GRPO training for models like Qwen2.5-7B-Instruct on datasets such as GSM8K with minimal configuration using torchforge.

Can I use vLLM for inference during PyTorch reinforcement learning?▼

vLLM is integrated for inference during PyTorch reinforcement learning workflows. This integration works alongside Monarch and TorchTitan to provide scalable distributed training and efficient inference generation.

How does torchforge isolate RL algorithm implementation from infrastructure?▼

Torchforge isolates RL algorithm implementation by providing clean abstractions that handle infrastructure separately. This allows you to focus purely on developing algorithms while the library manages distributed training and model synchronization.

Do I need Monarch and TorchTitan to run distributed RL training with this approach?▼

Monarch and TorchTitan are leveraged for scalable training across multiple GPUs and nodes. While they provide efficient distributed training capabilities, the core focus remains on clean RL abstractions, allowing you to experiment with algorithms easily.

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