Environment:Huggingface Trl vLLM Generation Environment

Knowledge Sources	TRL vLLM Integration
Domains	Infrastructure, Generation, Optimization
Last Updated	2026-02-06 17:00 GMT

Overview

Optional vLLM-accelerated generation environment requiring vLLM 0.10.2-0.12.0 with FastAPI, Pydantic, Requests, and Uvicorn for high-throughput inference during GRPO and RLOO training.

Description

This environment provides the vLLM backend for accelerated text generation during reinforcement learning training loops. TRL supports two vLLM modes: server mode (separate vLLM process communicating over HTTP) and colocate mode (vLLM shares GPU memory with the training process). The vLLM integration is primarily used by GRPOTrainer and RLOOTrainer to dramatically speed up the generation phase of the training loop. TRL applies several compatibility patches to handle version differences across the supported vLLM range.

Usage

Use this environment when setting use_vllm=True in GRPOConfig or RLOOConfig. Required for any workflow that needs high-throughput generation during RL-based training. In server mode, launch the vLLM server with trl vllm-serve before starting training.

System Requirements

Category	Requirement	Notes
OS	Linux	vLLM requires Linux; no Windows or macOS support
Hardware	NVIDIA GPU with CUDA	Minimum 16GB VRAM recommended for 7B models
Python	>= 3.10	Must match TRL core requirements
Network	Localhost ports 8000, 51216	Server mode uses HTTP; weight sync uses port 51216 by default

Dependencies

System Packages

`cuda-toolkit` (compatible with PyTorch/vLLM)

Python Packages

`vllm` >= 0.10.2, < 0.13.0 (supported: 0.10.2, 0.11.0, 0.11.1, 0.11.2, 0.12.0)
`fastapi`
`pydantic`
`requests`
`uvicorn`

Credentials

No additional credentials required beyond the base TRL environment. If the model is gated:

`HF_TOKEN`: Required to download gated models for vLLM serving.

Quick Install

# Install TRL with vLLM support
pip install "trl[vllm]"

# Or install vLLM dependencies separately
pip install "vllm>=0.10.2,<0.13.0" fastapi pydantic requests uvicorn

Code Evidence

Version check from `trl/import_utils.py:79-89`:

def is_vllm_available() -> bool:
    _vllm_available, _vllm_version = _is_package_available("vllm", return_version=True)
    if _vllm_available:
        if not (Version("0.10.2") <= Version(_vllm_version) <= Version("0.12.0")):
            warnings.warn(
                "TRL currently supports vLLM versions: 0.10.2, 0.11.0, 0.11.1, 0.11.2, 0.12.0. You have version "
                f"{_vllm_version} installed. We recommend installing a supported version to avoid compatibility "
                "issues.",
                stacklevel=2,
            )
    return _vllm_available

vLLM API version branching from `trl/generation/vllm_generation.py:46-49`:

if Version(vllm.__version__) <= Version("0.10.2"):
    from vllm.sampling_params import GuidedDecodingParams
else:
    from vllm.sampling_params import StructuredOutputsParams

Compatibility patches from `trl/_compat.py:77-82`:

def _patch_vllm_logging() -> None:
    """Set vLLM logging level to ERROR by default to reduce noise."""
    if _is_package_available("vllm"):
        import os
        os.environ["VLLM_LOGGING_LEVEL"] = os.getenv("VLLM_LOGGING_LEVEL", "ERROR")

Optional dependency definition from `pyproject.toml:83-89`:

vllm = [
    "vllm>=0.10.2,<0.13.0",
    "fastapi",
    "pydantic",
    "requests",
    "uvicorn"
]

Common Errors

Error Message	Cause	Solution
`TRL currently supports vLLM versions: 0.10.2...`	Unsupported vLLM version	Install a supported version: `pip install "vllm>=0.10.2,<0.13.0"`
`ConnectionError` after timeout	vLLM server not running (server mode)	Start the server first: `trl vllm-serve --model MODEL_ID`
`CUDA out of memory` in colocate mode	vLLM and training competing for GPU memory	Reduce `vllm_gpu_memory_utilization` (default 0.3) or use server mode
`DisabledTqdm` errors	vLLM < 0.11.1 bug	TRL auto-patches this; ensure you import trl before vllm

Compatibility Notes

vLLM < 0.11.1: Has a DisabledTqdm bug that TRL patches automatically.
vLLM < 0.12.0 + transformers >= 5.0: Has a cached tokenizer incompatibility that TRL patches via _patch_vllm_cached_tokenizer.
Server mode vs Colocate mode: Server mode requires a separate process but avoids GPU memory contention. Colocate mode is simpler but limits GPU memory for training.
DeepSpeed ZeRO-3: Disabling ds3_gather_for_generation is not compatible with vLLM generation.
VLLM_LOGGING_LEVEL: TRL automatically sets this to ERROR to reduce console noise. Override with export VLLM_LOGGING_LEVEL=INFO if needed.

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