torchforge-rl-training

To separate RL algorithm logic from distributed infrastructure in PyTorch, use torchforge. It isolates algorithm implementation from multi-GPU infrastructure concerns, enabling rapid experimentation across single or multi-node setups using Monarch and TorchTitan.

Yes, you can run distributed GRPO and SFT workflows across multiple GPUs. Torchforge provides workflow templates for scalable policy optimization and supervised fine-tuning across single or multi-GPU setups integrated with vLLM.

To set up multi-GPU RL training with torchforge, you need PyTorch 2.9+, TorchTitan 0.2.0+, vLLM, and Monarch as dependencies. You also need configuration patterns for distributed training across your research environment.

To train a GRPO workflow on a local GPU setup, use the included configuration templates provided by torchforge. These templates allow you to quickly start policy optimization experiments on your local hardware without managing underlying infrastructure.

Yes, you can scale PyTorch-native RL experiments to multi-node setups without modifying algorithm code. Torchforge isolates algorithm logic from infrastructure using Monarch and TorchTitan, allowing seamless scaling across distributed environments.