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torchforge-rl-training

Train PyTorch-native RL agents with GRPO, DAPO, and SAPO algorithms.

1|Updated Feb 21, 2026
Search Tags:#ai agents#reinforcement learning#pytorch#distributed training#torchtitan#torchforge#monarch
One-click install
npx skills add https://github.com/tianhao909/AI-Research-SKILLs-cn --skill torchforge-rl-training-tianhao909
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: torchforge-rl-training
Source: https://github.com/tianhao909/AI-Research-SKILLs-cn/tree/main/06-post-training/torchforge
Command: npx skills add https://github.com/tianhao909/AI-Research-SKILLs-cn --skill torchforge-rl-training-tianhao909

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill simplifies and scales Reinforcement Learning (RL) training for AI agents by providing a clean separation between RL algorithms and the underlying distributed infrastructure.

Core Features & Use Cases

  • PyTorch-Native: Leverages PyTorch for seamless integration without external dependencies like Ray.
  • Algorithm Experimentation: Enables rapid implementation and testing of RL algorithms (e.g., GRPO, DAPO) with minimal boilerplate code.
  • Scalable Training: Supports training from single-GPU setups to large-scale distributed environments using Monarch and TorchTitan.
  • Use Case: Train a custom RL agent to play a game or optimize a complex system by defining your reward function and algorithm logic, letting torchforge handle the distributed training and inference.

Quick Start

Launch GRPO training for the Qwen2.5-7B-Instruct model 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 run distributed reinforcement learning training for AI agents without using Ray?▼

PyTorch-native agentic reinforcement learning training can be scaled without Ray by using the torchforge library. It leverages Monarch and TorchTitan to provide clean RL abstractions and handle distributed training natively within the PyTorch ecosystem.

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

Experimenting with GRPO and DAPO algorithms is streamlined through PyTorch-native RL abstractions that minimize boilerplate code. The torchforge library enables rapid implementation and testing of these algorithms alongside SAPO for quick iteration.

Can I use vLLM for inference during distributed RL training?▼

vLLM is integrated directly into the PyTorch-native reinforcement learning workflow for inference. This integration works alongside TorchTitan for model parallelism, enabling scalable training and inference across distributed environments.

How do I scale reinforcement learning from a single-GPU setup to a large distributed environment?▼

Scaling reinforcement learning from single-GPU to large distributed environments is handled by Monarch and TorchTitan within the torchforge framework. This architecture separates RL algorithms from infrastructure, allowing seamless transitions in scale.

Does TorchTitan model parallelism support custom reward functions for game-playing agents?▼

TorchTitan model parallelism supports training custom game-playing agents by allowing developers to define specific reward functions and algorithm logic. The torchforge library manages the underlying distributed training infrastructure automatically.

Why do I need a clean separation between RL algorithms and distributed infrastructure?▼

A clean separation between RL algorithms and distributed infrastructure is needed to enable rapid algorithm experimentation without managing complex distributed systems. This abstraction allows researchers to focus on algorithm logic while torchforge handles scaling.

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