Provides guidance for PyTorch-native agentic RL using torchforge, Meta's library separating infra from algorithms. Use when you want clean RL abstractions, easy algorithm experimentation, or scalable training with Mon…
---
name: torchforge-rl-training
description: Provides guidance for PyTorch-native agentic RL using torchforge, Meta's library separating infra from algorithms. Use when you want clean RL abstractions, easy algorithm experimentation, or scalable training with Monarch and TorchTitan.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Reinforcement Learning, PyTorch, GRPO, SFT, Monarch, TorchTitan, Meta]
dependencies: [torch>=2.9.0, torchtitan>=0.2.0, vllm, monarch]
---
# torchforge: PyTorch-Native Agentic RL Library
torchforge is Meta's PyTorch-native RL library that separates infrastructure concerns from algorithm concerns. It enables rapid RL research by letting you focus on algorithms while handling distributed training, inference, and weight sync automatically.
## When to Use torchforge
**Choose torchforge when you need:**
- Clean separation between RL algorithms and infrastructure
- PyTorch-native abstractions (no Ray dependency)
- Easy algorithm experimentation (GRPO, DAPO, SAPO in ~100 lines)
- Scalable training with Monarch actor system
- Integration with TorchTitan for model parallelism
**Consider alternatives when:**
- You need production-ready stability → use **miles** or **verl**… load the full skill through Skill Me