torchforge-rl-training
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.
npx skills add Orchestra-Research/AI-Research-SKILLs --skill torchforge --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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** - You want Megatron-native training → use **slime** - torchforge is experimental and APIs may change ## Key Features - **Algorithm isolation**: Implement RL algorithms without touching infrastructure - **Scalability**: From single GPU to thousands via Monarch - **Modern stack**: TorchTitan (training), vLLM (inference), TorchStore (sync) - **Loss functions**: GRPO, DAPO, CISPO, GSPO, SAPO built-in ## Architecture Overview ``` ┌─────────────────────────────────
- When to Use torchforge
- Key Features
- Architecture Overview
- Installation
- ROCm Installation
- Quick Start
- SFT Training (2+ GPUs)
- GRPO Training (3+ GPUs)
- Workflow 1: GRPO Training for Math Reasoning
- Prerequisites Checklist
- Step 1: Create Configuration
- Step 2: Define Reward Function
- Step 3: Launch Training
- Step 4: Monitor Progress
Create environment
conda create -n forge python=3.12
conda activate forge
Install (handles PyTorch nightly + dependencies)
Verify
python -c "import torch, forge, vllm; print('OK')"
python -m apps.sft.main --config apps/sft/llama3_8b.yaml
python -m apps.grpo.main --config apps/grpo/qwen3_1_7b.yaml
python -m apps.grpo.main --config config/grpo_math.yaml
Submit jobWhat does the torchforge-rl-training skill do?
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.
How do I install it?
Run `npx skills add Orchestra-Research/AI-Research-SKILLs --skill torchforge --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From Orchestra-Research/AI-Research-SKILLs, a repository with 11,391 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.