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Vlm R1

VLM R1 is an Apache-2.0 Python research repository for R1-style vision-language models, with code, checkpoint links and demos for visual understanding.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Vlm R1 — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use VLM R1 when you need research code and model resources for training or adapting a vision-language model to visual reasoning tasks. The repository links to demos for trying REC and OVD examples.

Who it is for

  • Researchers exploring reinforcement learning for vision-language models.
  • Developers building prototypes for referring-expression comprehension, visual grounding or object detection.
  • Practitioners adapting QwenVL or InternVL models with the repository's training workflows.

Documented uses

  • Fine-tune a vision-language model with GRPO using full or LoRA training.
  • Train on referring-expression comprehension or open-vocabulary detection tasks.
  • Explore linked checkpoints and demos for REC and OVD.
  • Adapt training to custom data or multi-image inputs.
Before you choose

Requirements & limits

The public GitHub repository lists an Apache-2.0 license. Checkpoints and demos are linked separately through Hugging Face and ModelScope.

  • The repository provides training and adaptation code, not a documented hosted inference API.
  • Supported workflows include full and LoRA GRPO fine-tuning, frozen vision modules, multi-node training and multi-image input training.
  • The README identifies QwenVL and InternVL as supported model families.
  • REC and OVD demos and checkpoints are linked as separate resources.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up the VLM-R1 environment

Create and initialize the documented Conda environment for VLM-R1.

Before you start

  • Conda

Steps

  1. Create the environment with conda create -n vlm-r1 python=3.10.

  2. Activate it with conda activate vlm-r1.

  3. Run the repository setup script with the command below.

    Documented command
    bash setup.sh

Expected outcome

The documented environment setup commands have been run.

Guide limitations

  • These steps cover setup only; they do not describe running inference or training.
  • The README does not specify hardware requirements or confirm a successful setup.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
7 October 2026
Original website title
om-ai-lab/VLM-R1 · Official homepage title · retained in its source language
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