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torchforge-rl-training

Train PyTorch-native reinforcement learning algorithms with distributed infrastructure.

2|Updated Feb 4, 2026
Search Tags:#llm#reinforcement learning#pytorch#distributed training#grpo#torchtitan#monarch
One-click install
npx skills add https://github.com/zhuangbiaowei/smart_bot --skill torchforge-rl-training-zhuangbiaowei
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: torchforge-rl-training
Source: https://github.com/zhuangbiaowei/smart_bot/tree/main/skills/torchforge
Command: npx skills add https://github.com/zhuangbiaowei/smart_bot --skill torchforge-rl-training-zhuangbiaowei

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill simplifies the process of training Reinforcement Learning (RL) models using PyTorch by separating algorithm implementation from complex distributed training infrastructure.

Core Features & Use Cases

  • Clean RL Abstractions: Focus on RL algorithms without managing distributed systems.
  • Scalable Training: Leverage Monarch and TorchTitan for efficient training across multiple GPUs and nodes.
  • Algorithm Experimentation: Easily implement and test new RL algorithms like GRPO, DAPO, and SAPO.
  • Use Case: You want to fine-tune a large language model for a specific task using RL. This Skill provides the framework to define your reward function, implement the GRPO algorithm, and scale the training efficiently across your GPU cluster.

Quick Start

Use the torchforge-rl-training 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 scale PyTorch reinforcement learning training across multiple GPUs?▼

To scale PyTorch reinforcement learning training across multiple GPUs, this Skill leverages Monarch and TorchTitan to abstract distributed infrastructure, enabling efficient multi-node execution without managing complex systems directly.

Can I implement GRPO for LLM fine-tuning using PyTorch?▼

Yes, you can implement GRPO for LLM fine-tuning using PyTorch. This Skill provides clean RL abstractions to define reward functions and execute GRPO algorithms without handling distributed training overhead.

Do I need vLLM and Monarch to run PyTorch RL training?▼

Yes, you need vLLM and Monarch to run this PyTorch RL training Skill. These dependencies, alongside TorchTitan, are required to separate algorithm implementation from distributed infrastructure and enable scalable execution.

What is the best way to separate RL algorithms from distributed infrastructure in PyTorch?▼

The best way to separate RL algorithms from distributed infrastructure in PyTorch is using this Skill's abstractions, which allow researchers to focus on algorithms like GRPO, DAPO, and SAPO while TorchTitan manages scaling.

How do I start GRPO training for math reasoning with PyTorch?▼

To start GRPO training for math reasoning with PyTorch, use the provided configuration scripts in this Skill to launch the process, defining your reward function while the framework handles distributed execution.

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