Instructions to use RLWRLD/RLDX-1-VLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RLWRLD/RLDX-1-VLM with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="RLWRLD/RLDX-1-VLM")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("RLWRLD/RLDX-1-VLM")
model = AutoModelForImageTextToText.from_pretrained("RLWRLD/RLDX-1-VLM")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use RLWRLD/RLDX-1-VLM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RLWRLD/RLDX-1-VLM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RLWRLD/RLDX-1-VLM",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/RLWRLD/RLDX-1-VLM

SGLang

How to use RLWRLD/RLDX-1-VLM with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "RLWRLD/RLDX-1-VLM" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RLWRLD/RLDX-1-VLM",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "RLWRLD/RLDX-1-VLM" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RLWRLD/RLDX-1-VLM",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use RLWRLD/RLDX-1-VLM with Docker Model Runner:
```
docker model run hf.co/RLWRLD/RLDX-1-VLM
```

RLDX-1-VLM / README.md

jaehyunkang

RLDX-1 Release

4b9f870 22 days ago

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metadata

license: other
license_name: rlwrld-model-license-v1.0
license_link: LICENSE.md
library_name: transformers
pipeline_tag: image-text-to-text
tags:
  - vlm
  - vision-language
  - rldx
  - qwen3-vl
base_model: Qwen/Qwen3-VL-8B-Instruct

RLDX-1-VLM

Paper · Project page · Code · Models

RLDX-1-VLM is the vision-language backbone used by the RLDX-1 robot policy family. It is a Qwen3-VL-8B-Instruct checkpoint distributed separately from the action policy so that researchers can inspect, finetune, or replace the perceptual stack independently.

Note. This checkpoint exposes a standard Qwen3-VL VLM interface only — it does not ship the Multi-Stream Action Transformer head, the cognition tokens, the memory / motion / physics modules, or the RLDX inference server. For action prediction, use one of the RLDX-1-PT, RLDX-1-FT-*, or RLDX-1-MT-* checkpoints.

Intended use

As the --backbone-path for finetuning a fresh RLDX-1 policy (recipe).
For VLM-only ablations, dense-captioning experiments, or perceptual probing within the RLDX research stack.

Quick start

from transformers import AutoModelForImageTextToText, AutoProcessor

model = AutoModelForImageTextToText.from_pretrained(
    "RLWRLD/RLDX-1-VLM",
    torch_dtype="bfloat16",
    device_map="cuda:0",
)
processor = AutoProcessor.from_pretrained("RLWRLD/RLDX-1-VLM")

Model details

Type: Vision-language model (multimodal text + image / video).
Backbone: Qwen/Qwen3-VL-8B-Instruct.
Params: 8B.
Role in RLDX-1: perceptual encoder for the MSAT action policy. Cognition tokens are injected into this backbone and routed through Qwen3-VL hidden states to produce a compact perceptual summary consumed by the action model.

For a full architectural walkthrough including how cognition tokens are wired into this backbone, see docs/architecture.md.

Limitations

RLDX-1-VLM is a research backbone snapshot. It is not safety-tuned beyond its Qwen3-VL upstream, and it is not intended as a general-purpose chat assistant. For action prediction it must be paired with the RLDX-1 policy head; the standalone VLM does not produce robot commands.

Citation

@article{rldx2026,
  title={RLDX-1 Technical Report},
  author={Kim, Dongyoung and Jang, Huiwon and Koo, Myungkyu and Jang, Suhyeok and Kim, Taeyoung and others},
  year={2026},
  note={RLWRLD},
  eprint={2605.03269},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2605.03269}
}

License

Released under the RLWRLD Model License v1.0 — a non-commercial license with attribution and share-alike requirements. See LICENSE.md for the full text. By using this model you agree to those terms, including the use restrictions in §3.5.