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torchforge-rl-training

Train PyTorch-native agentic reinforcement learning algorithms with torchforge.

3|Updated Feb 21, 2026
Search Tags:#llm#reinforcement learning#pytorch#distributed training#torchforge#meta ai
One-click install
npx skills add https://github.com/ihatesea69/HieuNghi-AI-Skills --skill torchforge-rl-training-ihatesea69
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: torchforge-rl-training
Source: https://github.com/ihatesea69/HieuNghi-AI-Skills/tree/main/airesearch_skills/06-post-training/torchforge
Command: npx skills add https://github.com/ihatesea69/HieuNghi-AI-Skills --skill torchforge-rl-training-ihatesea69

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill simplifies Reinforcement Learning (RL) research and development by providing a PyTorch-native framework that separates RL algorithms from complex distributed training infrastructure.

Core Features & Use Cases

  • Algorithm Isolation: Implement RL algorithms without managing distributed systems.
  • Scalable Training: Supports training from single GPUs to thousands using Monarch and TorchTitan.
  • Modern Stack: Integrates with TorchTitan for training, vLLM for inference, and TorchStore for weight synchronization.
  • Use Case: Rapidly experiment with new RL algorithms like GRPO or custom loss functions for tasks such as math reasoning or game playing, leveraging Meta's advanced infrastructure.

Quick Start

Launch GRPO training for math reasoning using the provided configuration file.

Frequently Asked Questions about torchforge-rl-training

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

FAQPage Schema
How do I isolate reinforcement learning algorithms from distributed training infrastructure?▼

To isolate reinforcement learning algorithms from distributed systems, you can use this PyTorch-native framework to implement algorithms like GRPO while the Monarch and TorchTitan integrations handle scalable distributed training automatically.

Can I scale PyTorch-native RL training from a single GPU to thousands?▼

You can scale PyTorch-native reinforcement learning training from a single GPU to thousands by leveraging Monarch and TorchTitan for distributed orchestration, alongside vLLM for inference and TorchStore for weight synchronization.

What is the best way to experiment with GRPO and custom loss functions for AI research?▼

The best way to experiment with GRPO and custom loss functions for AI research is using clean RL abstractions that separate algorithm logic from infrastructure, enabling rapid experimentation for tasks like math reasoning or game playing.

Does this framework support Supervised Fine-Tuning alongside reinforcement learning?▼

Yes, the framework supports Supervised Fine-Tuning (SFT) alongside reinforcement learning algorithms like GRPO, allowing you to integrate both training methods within your Meta torchforge-based AI research workflows.

How does weight synchronization work during distributed reinforcement learning?▼

Weight synchronization during distributed reinforcement learning is managed through TorchStore, which integrates with the TorchTitan training stack and vLLM inference engine to maintain consistency across scaling boundaries.

Do I need Meta's torchforge to run scalable agentic RL training?▼

Yes, you need Meta's torchforge library to run scalable agentic reinforcement learning training, as it provides the foundational PyTorch-native abstractions required to interface with Monarch and TorchTitan.

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