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torchforge-rl-training

Train PyTorch-native reinforcement learning agents across distributed systems.

1|Updated Feb 10, 2026
Search Tags:#agent training#reinforcement learning#pytorch#distributed training#torchtitan#rl algorithms#monarch
One-click install
npx skills add https://github.com/informatico-madrid/Architect-Expert-Gap-Forge --skill torchforge-rl-training-informatico-madrid
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: torchforge-rl-training
Source: https://github.com/informatico-madrid/Architect-Expert-Gap-Forge/tree/main/.github/skills/torchforge
Command: npx skills add https://github.com/informatico-madrid/Architect-Expert-Gap-Forge --skill torchforge-rl-training-informatico-madrid

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill streamlines the process of training Reinforcement Learning agents using PyTorch, abstracting away complex distributed infrastructure and allowing researchers to focus on algorithm development.

Core Features & Use Cases

  • PyTorch-Native RL: Implement RL algorithms without external dependencies like Ray.
  • Scalable Training: Supports training from single-GPU setups to large-scale distributed environments using Monarch and TorchTitan.
  • Algorithm Experimentation: Provides clean abstractions for rapid experimentation with algorithms like GRPO, DAPO, and SAPO.
  • Use Case: You need to quickly prototype and train a new RL algorithm for a complex game, leveraging multiple GPUs for faster iteration and robust evaluation.

Quick Start

Use the torchforge skill to launch GRPO training for math reasoning using the provided configuration.

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 using PyTorch without external dependencies like Ray?▼

You can train reinforcement learning agents using PyTorch-native interfaces that abstract away complex distributed infrastructure. This Skill separates infrastructure from algorithms, enabling rapid research without external dependencies like Ray.

Can I scale PyTorch reinforcement learning training across multiple GPUs?▼

Yes, you can scale PyTorch reinforcement learning training from single-GPU setups to large-scale distributed systems. It uses Monarch and TorchTitan to support scalable training across distributed environments.

What RL algorithms are supported for PyTorch-native agent training?▼

PyTorch-native agent training supports algorithms like GRPO, DAPO, and SAPO. These algorithms are provided with clean abstractions to facilitate rapid experimentation and robust evaluation.

Does distributed reinforcement learning with Monarch and TorchTitan separate infrastructure from algorithm development?▼

Yes, distributed reinforcement learning with Monarch and TorchTitan explicitly separates infrastructure from algorithms. This separation allows researchers to focus on algorithm development while the system manages scalable training.

What is the best way to prototype a new RL algorithm for a complex game using multiple GPUs?▼

The best way to prototype a new RL algorithm for a complex game is using clean abstractions that leverage multiple GPUs for faster iteration. This Skill provides PyTorch-native interfaces to quickly experiment and train algorithms like GRPO for math reasoning.

Do I need Ray to run distributed training for PyTorch reinforcement learning algorithms?▼

No, you do not need Ray to run distributed training for PyTorch reinforcement learning algorithms. This Skill provides a PyTorch-native implementation that handles scalable training using Monarch and TorchTitan instead of external dependencies.

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