Principle:OpenRLHF OpenRLHF Ray Cluster Initialization
| Knowledge Sources | |
|---|---|
| Domains | Distributed_Computing, Training_Infrastructure |
| Last Updated | 2026-02-07 00:00 GMT |
Overview
A distributed computing initialization pattern that sets up a Ray cluster with GPU placement groups for multi-model RLHF training.
Description
Ray Cluster Initialization creates the distributed infrastructure for PPO training where multiple models (actor, critic, reward model, reference model, vLLM engines) run on different GPU groups. It connects to a Ray cluster, creates placement groups that reserve specific GPU counts for each model role, and enables efficient inter-model communication.
Usage
Used in PPO and Math-GRPO workflows. Not used for simpler workflows (SFT, RM, DPO, KD) which use only DeepSpeed.
Theoretical Basis
Placement Groups: Ray's mechanism for co-locating or distributing actors across nodes. In OpenRLHF PPO:
- Actor placement group: GPUs for the policy model (DeepSpeed training)
- Critic placement group: GPUs for the value function model
- Reward model placement group: GPUs for reward scoring
- vLLM placement group: GPUs for fast text generation
This separation enables each model to use the optimal parallelism strategy independently.