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torchforge-rl-training

Prototype and train PyTorch RL algorithms with GRPO, DAPO, and SAPO.

6|3|Updated Jan 29, 2026
Search Tags:#PyTorch#distributed training#RL#Reinforcement Learning#algorithm experimentation#RL libraries
One-click install
npx skills add https://github.com/jonnabio/ace-framework --skill torchforge-rl-training-jonnabio
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: torchforge-rl-training
Source: https://github.com/jonnabio/ace-framework/tree/main/.ace/packs/ai-research/torchforge
Command: npx skills add https://github.com/jonnabio/ace-framework --skill torchforge-rl-training-jonnabio

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires torch, torchtitan, monarch, vllm, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill empowers researchers and engineers to rapidly prototype, train, and scale Reinforcement Learning (RL) models using PyTorch with ease.

Core Features & Use Cases

  • Clean Abstractions: Separate RL algorithms from infrastructure concerns for flexible research.
  • Scalable Training: Integrate with Monarch and TorchTitan for scalable, distributed training across GPUs.
  • Easy Experimentation: Quick implementation of common RL algorithms with GRPO, DAPO, and SAPO in less than 100 lines of code.
  • Use Case: Develop and refine an RL model for automated robot navigation with precise, high-precision movements.

Quick Start

To get started, create an environment with the necessary packages and execute the training script:

python -m apps.grpo.main --config config/grpo_math.yaml

Frequently Asked Questions about torchforge-rl-training

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

FAQPage Schema
How do I prototype PyTorch reinforcement learning algorithms for rapid experimentation?▼

PyTorch reinforcement learning prototyping is streamlined by providing clean abstractions that separate algorithms from infrastructure, enabling quick implementation of GRPO, DAPO, and SAPO in under 100 lines of code.

Can I use vLLM for distributed reinforcement learning training across multiple GPUs?▼

Distributed reinforcement learning training across GPUs is supported through integration with vLLM, Monarch, and TorchTitan, providing scalable infrastructure abstraction for PyTorch-native models.

What is the best way to run GRPO training for a math reasoning model in PyTorch?▼

Running GRPO training for math reasoning involves executing the provided training script with a configuration file, such as running the grpo_math.yaml config via the command line interface.

Does PyTorch reinforcement learning require separating algorithm logic from infrastructure setup?▼

PyTorch reinforcement learning benefits from clean abstractions that separate RL algorithms from infrastructure concerns, allowing researchers to focus on algorithm experimentation without managing distributed training complexities.

What dependencies do I need to install before starting PyTorch RL algorithm experimentation?▼

PyTorch RL algorithm experimentation requires installing torch, torchtitan, monarch, and vllm packages to enable rapid prototyping, infrastructure abstraction, and distributed training capabilities.

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