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torchforge-rl-training

Facilitates PyTorch-native RL training with Monarch, TorchTitan, and vLLM for distributed execution and inference.

11.5k|842|Updated Nov 3, 2025
Search Tags:#mlops#reinforcement learning#pytorch#inference#distributed training#rl algorithms
One-click install
npx skills add https://github.com/Orchestra-Research/AI-Research-SKILLs --skill torchforge-rl-training
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: torchforge-rl-training
Source: https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/main/06-post-training/torchforge
Command: npx skills add https://github.com/Orchestra-Research/AI-Research-SKILLs --skill torchforge-rl-training

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill simplifies Reinforcement Learning (RL) development and training in PyTorch by abstracting away complex infrastructure concerns, allowing researchers to focus on algorithms.

Core Features & Use Cases

  • PyTorch-Native RL: Implement RL algorithms directly in PyTorch without external dependencies like Ray.
  • Scalable Training: Supports training from single-GPU setups to large-scale distributed clusters using Monarch and TorchTitan.
  • Algorithm Experimentation: Provides clean abstractions for rapid experimentation with RL algorithms like GRPO, DAPO, and SAPO.
  • Use Case: Train a custom RL agent for a game or simulation by defining your reward function and using torchforge to handle distributed training, inference, and weight synchronization.

Quick Start

Launch GRPO training for Qwen3.1.7B 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 set up distributed PyTorch reinforcement learning training without Ray?▼

You can implement distributed PyTorch reinforcement learning natively without Ray by using Monarch and TorchTitan for execution. This Skill abstracts infrastructure concerns, letting you scale training from single-GPU setups to large clusters.

Can I use vLLM for inference during reinforcement learning algorithm training?▼

Yes, vLLM is integrated for inference during reinforcement learning training. The Skill separates inference infrastructure from RL algorithms like GRPO, DAPO, and SAPO, handling weight synchronization automatically.

What is the best way to experiment with GRPO and SAPO algorithms in PyTorch?▼

The best way to experiment with GRPO and SAPO algorithms is using clean PyTorch abstractions that separate algorithmic logic from infrastructure. This Skill provides those abstractions for rapid RL research and scalable execution.

Does this PyTorch RL training approach support custom reward functions for simulations?▼

Yes, you can train custom RL agents for simulations by defining your own reward functions. The Skill handles distributed training, vLLM inference, and weight synchronization automatically based on your configuration.

Why use Monarch and TorchTitan instead of Ray for scalable reinforcement learning?▼

Monarch and TorchTitan provide PyTorch-native distributed execution for scalable reinforcement learning without external dependencies. This approach keeps your stack modern and eliminates the integration overhead required when using Ray.

What are the limitations of separating infrastructure from RL algorithms in PyTorch?▼

Separating infrastructure from RL algorithms requires understanding PyTorch-native distributed execution concepts. Users must configure Monarch and TorchTitan properly, though the Skill provides reference scripts and configuration files to guide setup.

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