Principle:OpenRLHF OpenRLHF vLLM Inference Engine
| Knowledge Sources | |
|---|---|
| Domains | Inference, Training_Infrastructure |
| Last Updated | 2026-02-07 00:00 GMT |
Overview
A high-throughput text generation engine that uses PagedAttention for efficient KV-cache management during on-policy sample generation in RLHF.
Description
vLLM Inference Engine provides optimized text generation for RL training. On-policy methods (PPO, GRPO) require generating many responses per training step, making generation speed critical. vLLM uses PagedAttention to manage the KV-cache memory efficiently, enabling higher batch sizes and throughput than naive HuggingFace generation.
In OpenRLHF, vLLM engines run as separate Ray actors with their own GPU allocation, and their weights are synchronized from the training actor after each PPO update.
Usage
Used in PPO and Math-GRPO workflows for fast on-policy generation. Also used in rejection sampling and iterative DPO for batch generation.
Theoretical Basis
PagedAttention: Manages KV-cache memory using virtual memory paging:
- Allocates KV-cache in fixed-size blocks (pages)
- Dynamically maps logical positions to physical blocks
- Reduces memory waste from 60-80% to near-zero
- Enables 2-4x higher throughput than naive generation