RynnBrain 1.1

August 10, 2026 · View on GitHub

Towards More Capable and Generalizable Embodied Foundation Models

💫 Project Page   |   🤗 Hugging Face   |   🤖 ModelScope   |   📚 Cookbooks   |   📄 arXiv   |   📄 1.1 paper

News

  • [2026.07.16] ✨✨ Release RynnBrain 1.1 checkpoints (2B / 9B / 122B-A10B) on Hugging Face & ModelScope!
  • [2026.07.16] ✨✨ Release RynnBrain 1.1 Technical Report !
  • [2026.07.16] ✨✨ Release RynnBrain 1.1! To access the resources of RynnBrain 1.0, please checkout to the rynnbrain1.0 branch.
  • [2026.04.13] 🔥🔥 Launch RynnBrain-4B !!
  • [2026.02.17] 🔥🔥 Release RynnBrain 1.0 Technical Report !!
  • [2026.02.09] 🔥🔥 Release code and model checkpoints of RynnBrain 1.0!!

Introduction

We present RynnBrain 1.1, a systematic upgrade of RynnBrain for embodied intelligence. RynnBrain 1.1 is released in three scales: 2B, 9B, and 122B-A10B, extending the model family from compact dense models to its first 122B-level sparse-MoE model.

What's New in 1.1 🚀

  • Unified Embodied Scaling to 122B: Establishes the first embodied brain model at the 122B scale under a unified training recipe shared across 2B, 9B, and 122B-A10B, enabling a systematic study of how embodied cognition, spatial reasoning, grounding, and planning evolve with scale.
  • Native 3D and contact point grounding: Introduces explicit 3D-grounded training and a new contact point prediction task, extending RynnBrain from image-plane localization to metric 3D understanding and action-relevant interaction grounding.
  • Real-robot VLA transfer: Bridges perception and action through RynnBrain-VLA, translating embodied understanding into real-robot control and demonstrating strong cross-platform generalization on Unitree G1, Astribot, and Tianji-Wuji across humanoid, bimanual, and dexterous-hand tasks.

Model Architecture

RynnBrain 1.1 adopts a unified decoder-only vision-language architecture across all scales, supporting both dense and sparse-MoE variants. It encodes omni-vision inputs with language instructions and produces aligned outputs — text, pointing sequences, 3D perception, and contact signals — enabling egocentric understanding, spatio-temporal grounding, physical-world reasoning, and fine-grained planning.

Performance

  • General Embodied Understanding

RynnBrain 1.1-2B vs. other 2B-scale models

RynnBrain 1.1-9B vs. other 9B-scale models

RynnBrain 1.1-122B vs. other 122B-scale models

  • Real-Robot VLA Evaluation

Real-robot VLA success rates vs. baselines

Real-robot deployment demos

  • 3D Grounding

3D grounding accuracy vs. baselines

  • Contact Point Prediction

Contact point prediction visualization

Model Zoo

ModelBase ModelHuggingFaceModelScope
RynnBrain1.1-2BQwen3.5-2BLinkLink
RynnBrain1.1-9BQwen3.5-9BLinkLink
RynnBrain1.1-122B-A10BQwen3.5-122B-A10BLinkLink

Quick Start

Inference with 🤗 transformers

Minimal dependencies

pip install transformers==5.2.0

Run text generation

import torch
from transformers import AutoModelForImageTextToText, AutoProcessor

conversation = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "cookbooks/assets/object_location/images/000000086408.jpg"},
            {
                "type": "text",
                "text": "What appliance can be used to heat food quickly.\nGenerate coordinates for one object bounding box. Constraints: x1,y1,x2,y2 in [0,1000]. Response must be in the format: <object> (x1, y1), (x2, y2) </object>",
            },
        ],
    }
]

model_path = "Alibaba-DAMO-Academy/RynnBrain1.1-2B"
processor = AutoProcessor.from_pretrained(model_path)

model = AutoModelForImageTextToText.from_pretrained(
    model_path,
    dtype=torch.bfloat16,
)
model.to("cuda")

model_inputs = processor.apply_chat_template(
    conversation,
    add_generation_prompt=True,
    enable_thinking=False,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
)
model_inputs = model_inputs.to("cuda")

output_ids = model.generate(
    **model_inputs,
    max_new_tokens=256,
    do_sample=False,
)
output_ids = output_ids[:, model_inputs["input_ids"].size(1) :]
response = processor.decode(output_ids[0], skip_special_tokens=True)
print(response)

Inference with SGLang

For installation and advanced usages, please refer to the official documentation.

OpenAI-Compatible Serving

# launch server
python3 -m sglang.launch_server --model-path Alibaba-DAMO-Academy/RynnBrain1.1-2B --host 0.0.0.0 --port 8000

# inference using openai api
import base64
import io

from openai import OpenAI
from PIL import Image

def pil_to_url(image: Image.Image):
    image_format = image.format if image.format else 'PNG'
    buffered = io.BytesIO()
    image.save(buffered, format=image_format)
    img_str = base64.b64encode(buffered.getvalue()).decode('utf-8')
    return f'data:image/{image_format.lower()};base64,{img_str}'

messages = [
    {
        'role': 'user',
        'content': [
            {'type': 'image_url', 'image_url': {'url': pil_to_url(Image.open('cookbooks/assets/object_location/images/000000086408.jpg'))}},
            {'type': 'text', 'text': 'What appliance can be used to heat food quickly.\nGenerate coordinates for one object bounding box. Constraints: x1,y1,x2,y2 ∈ [0,1000]. Response must be in the format: <object> (x1, y1), (x2, y2) </object>'},
        ],
    }
]

client = OpenAI(api_key="", base_url="http://localhost:8000/v1")
response = client.chat.completions.create(
    model="default",
    messages=messages,
    stream=False,
).choices[0].message.content
print(response)

Offline Engine

import sglang as sgl
from transformers import AutoProcessor

def main():
    conversation = [
        {
            'role': 'user',
            'content': [
                {'type': 'image'},
                {'type': 'text', 'text': 'What appliance can be used to heat food quickly.\nGenerate coordinates for one object bounding box. Constraints: x1,y1,x2,y2 ∈ [0,1000]. Response must be in the format: <object> (x1, y1), (x2, y2) </object>'},
            ],
        }
    ]

    model_path = 'Alibaba-DAMO-Academy/RynnBrain1.1-2B'
    llm = sgl.Engine(model_path=model_path)
    processor = AutoProcessor.from_pretrained(model_path)

    prompt = processor.apply_chat_template(
        conversation,
        add_generation_prompt=True,
        enable_thinking=False,
        tokenize=False,
    )

    output = llm.generate(
        prompt=prompt,
        image_data='cookbooks/assets/object_location/images/000000086408.jpg',
        sampling_params={"temperature": 0.8, "top_p": 0.95},
    )
    print(f"Prompt: {prompt}\nGenerated text: {output['text']}")

if __name__ == '__main__':
    main()

Cookbooks

Check out the cookbooks that showcase RynnBrain's capabilities in cognition, localization, reasoning, and planning.

CategoryCookbook nameDescription
Spatial Understanding1_spatial_understanding.ipynbShows the model's ability for spatial understanding in the video scene.
Object Understanding2_object_understanding.ipynbShows how the model understands object categories, attributes, and relations and counting ability.
Object Grounding3_object_grounding.ipynbLocates specific objects with bounding boxes in an image or video based on instructions.
Area Location4_area_location.ipynbIdentifies and marks specified regions by points in an image or video.
Affordance Location5_affordance_location.ipynbFinds areas or objects with specific affordances in an image or video.
Trajectory Location6_trajectory_location.ipynbInfers and annotates trajectories or motion paths in an image or video.
🆕 Contact Point Prediction7_contact_point_prediction.ipynbPredicts an instruction-conditioned contact point and in-plane orientation from an image.
🆕 3D Grounding8_3d_grounding.ipynbPredicts 3D bounding boxes (position, dimensions, orientation) from a single RGB image with camera intrinsics.

Training

Pretraining & Evaluation

Please refer to RynnScale for details of pretraining and evaluation.

Note that thinking mode is disabled by default for all benchmarks, unless otherwise specified.

From RynnBrain 1.0

Finetuning recipes and the benchmark introduced with RynnBrain 1.0, which remain fully compatible with the base model.

Finetuning

  • Reasoning: An interleaved reasoning approach that fuses spatial grounding with textual cues directly over egocentric video streams, bridging the gap between language and the physical world to keep reasoning firmly grounded in reality.

  • Navigation: A vision-language navigation model fine-tuned on the RynnBrain base model. Empirically, fine-tuning on RynnBrain yields consistently stronger navigation performance than fine-tuning on other foundation models.

  • Planning: RynnBrain embeds the locations of affordances, areas, and objects directly into its planning outputs, allowing even highly intricate, fine-grained tasks to be handled within our hierarchical RynnBrain-VLA system.

RynnBrain-Bench

RynnBrain-Bench is a high-dimensional benchmark for embodied understanding, evaluating models across four key dimensions—object cognition, spatial cognition, grounding, and pointing—with an emphasis on fine-grained understanding and spatio-temporal localization over episodic video sequences. For details, please refer to RynnBrain-Bench.

📑 Citation

If you find RynnBrain useful for your research and applications, please cite using this BibTeX:

@article{damo2026rynnbrain,
  title={RynnBrain: Open Embodied Foundation Models},
  author={Ronghao Dang, Jiayan Guo, Bohan Hou, Sicong Leng, Kehan Li, Xin Li, Jiangpin Liu, Yunxuan Mao, Zhikai Wang, Yuqian Yuan, Minghao Zhu, Xiao Lin, Yang Bai, Qian Jiang, Yaxi Zhao, Minghua Zeng, Junlong Gao, Yuming Jiang, Jun Cen, Siteng Huang, Liuyi Wang, Wenqiao Zhang, Chengju Liu, Jianfei Yang, Shijian Lu, Deli Zhao},
  journal={arXiv preprint arXiv:2602.14979v1},
  year={2026},
  url = {https://arxiv.org/abs/2602.14979v1}
}

@article{damo2026rynnbrain11,
  title={RynnBrain 1.1: Towards More Capable and Generalizable Embodied Foundation Model},
  author={Kehan Li, Bohan Hou, Minghao Zhu, Tianyi Zhang, Zesen Cheng, Zhikai Wang, Sicong Leng, Xin Li, Xiao Lin, Biying Yao, Minghua Zeng, Jiangpin Liu, Ronghao Dang, Jiayan Guo, Siteng Huang, Haoyu Zhao, Heng Ping, Yaxi Zhao, Kexiang Wang, Tong Lu, Shengke Xue, Jiahao Tang, Yulei Wang, Zejing Wang, Jianwei Gao, Shijian Lu, Chengju Liu, Jianfei Yang, Mingxiu Chen, Deli Zhao},
  journal={arXiv preprint arXiv:2607.17977},
  year={2026},
  url = {https://arxiv.org/abs/2607.17977}
}
💡 Some other multimodal-LLM projects from our team may interest you ✨.

RynnEC: Bringing MLLMs into Embodied World
Ronghao Dang*, Yuqian Yuan*, Yunxuan Mao*, Kehan Li*, Jiangpin Liu, Zhikai Wang, Fan Wang, Deli Zhao, Xin Li
github github arXiv

RynnScale
RynnScale Team
github github

RynnWorld-4D: 4D Embodied World Models for Robotic Manipulation
Haoyu Zhao, Xingyue Zhao, Siteng Huang, Xin Li, Deli Zhao, Zhongyu Li
github github arXiv

RynnWorld-Teleop: An Action-Conditioned World Model for Digital Teleoperation
Haoyu Zhao, Xingyue Zhao, Hangyu Li, Biao Gong, Kehan Li, Siteng Huang, Xin Li, Deli Zhao, Zhongyu Li
github github arXiv

RynnVLA-001: Using Human Demonstrations to Improve Robot Manipulation
Yuming Jiang, Siteng Huang, Shengke Xue, Yaxi Zhao, Jun Cen, Sicong Leng, Kehan Li, Jiayan Guo, Kexiang Wang, Mingxiu Chen, Fan Wang, Deli Zhao, Xin Li
github github arXiv

RynnVLA-002: A Unified Vision-Language-Action and World Model
Jun Cen, Siteng Huang, Yuqian Yuan, Kehan Li, Hangjie Yuan, Chaohui Yu, Yuming Jiang, Jiayan Guo, Xin Li, Hao Luo, Fan Wang, Deli Zhao, Hao Chen
github github arXiv

RynnRCP: Open Robotics Context Protocol and RobotMotion
RynnBot Team
github github

RynnMotion: All-In-One Toolkit for Fast Robot Prototyping and Heterogeneous Teleoperation
RynnBot Team
github github

Acknowledgement

Our RynnBrain is built on top of Qwen3-VL and Qwen3.5. We also learned a lot from the implementation of pi 0.5 and RTC. If your work is used in RynnBrain but not mentioned in either this repo or the technical report, feel free to let us know :heart:.

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.