Environment:Allenai Open instruct vLLM Inference

Knowledge Sources	Open Instruct vLLM
Domains	Inference, Reinforcement_Learning
Last Updated	2026-02-07 00:00 GMT

Overview

vLLM 0.14.1 inference engine environment required for GRPO generation and async rollout collection.

Description

The GRPO training workflow uses vLLM as the inference engine for generating rollouts from the policy model. vLLM runs as a Ray actor (`LLMRayActor`) with dedicated GPUs, using Flash Attention backend and V1 API. It supports weight synchronization from the training process via Ray collective communication or NCCL process groups. The engine has configurable GPU memory utilization (default 90%) and prefix caching support.

Usage

Use this environment for GRPO reinforcement learning training. vLLM provides high-throughput generation for rollout collection, which is the primary bottleneck in on-policy RL training. Not required for SFT, DPO, or Reward Model training.

System Requirements

Category	Requirement	Notes
OS	Linux (x86_64)	vLLM not supported on macOS or aarch64
Hardware	NVIDIA GPU with CUDA	Dedicated GPUs for inference (separate from training GPUs)
VRAM	Model-dependent	Must fit model in vLLM's memory pool

Dependencies

Python Packages

`vllm` == 0.14.1
`ray[default]` >= 2.49.2
`flash-attn` >= 2.8.3
`torch` >= 2.9.0

Environment Variables

`VLLM_USE_V1` = 1 (use V1 engine API)
`VLLM_DISABLE_COMPILE_CACHE` = 1 (avoid stale compile cache)
`VLLM_ATTENTION_BACKEND` = FLASH_ATTN
`VLLM_ALLOW_INSECURE_SERIALIZATION` = 1 (for Ray weight transfer)
`VLLM_LOGGING_LEVEL` = WARNING

Credentials

`HF_TOKEN`: Required if loading gated models from HuggingFace Hub.

Quick Install

# vLLM is installed as part of the main project on Linux
uv sync

# Manual install (Linux only)
pip install vllm==0.14.1 ray[default]>=2.49.2 flash-attn>=2.8.3

Code Evidence

vLLM availability check from `conftest.py:5-10`:

try:
    import vllm  # noqa: F401
    VLLM_AVAILABLE = True
except ImportError:
    VLLM_AVAILABLE = False

Timeout constants from `vllm_utils.py:74-75`:

INFERENCE_INIT_TIMEOUT_S = 1200  # 20 minutes for engine initialization
VLLM_HEALTH_CHECK_TIMEOUT_S = 600.0  # 10 minutes for health checks

GPU memory utilization default from `data_loader.py:285`:

vllm_gpu_memory_utilization: float = 0.9

v0 caching warning from `data_loader.py:290-293`:

if os.environ.get("VLLM_USE_V1") == "0":
    logger.warning("When using the v0 version of vLLM, caching is broken...")
    if self.vllm_enable_prefix_caching:
        raise ValueError("Prefix caching is currently not supported for v0.")

Common Errors

Error Message	Cause	Solution
`ImportError: vllm not found`	vLLM not installed (macOS)	Use Linux for GRPO training
`CUDA out of memory` in vLLM	GPU memory utilization too high	Reduce `vllm_gpu_memory_utilization` to 0.8 or 0.7
vLLM health check timeout	Large model loading on first init	Increase VLLM_HEALTH_CHECK_TIMEOUT_S; allow 20 minutes for init
Prefix caching error with v0	Using vLLM v0 API with prefix caching	Set `VLLM_USE_V1=1` or disable prefix caching

Compatibility Notes

macOS: vLLM is completely excluded via platform marker. GRPO training is not possible on macOS.
ARM Linux (aarch64): vLLM support depends on the vLLM release; check vLLM docs.
vLLM V1 vs V0: V1 is the default. V0 has known caching bugs and lacks prefix caching support.
Weight sync: Uses Ray collective or NCCL process groups for transferring updated weights from training to inference.

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