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<p align="center">
<h1 align="center">[CVPR 2026] Monet: Reasoning in Latent Visual Space Beyond Images and Language</h1>
<p align="center">
</p>
<p align="center">
<a href="https://novaglow646.github.io/">Qixun Wang</a>,
<a href="https://frankyang-17.github.io/">Yang Shi</a>,
<a href="https://yifeiwang77.com/">Yifei Wang</a>,
<a href="https://scholar.google.com/citations?user=COdftTMAAAAJ&hl=en">Yuanxing Zhang</a>,
<a href="https://magicwpf.github.io/">Pengfei Wan</a>,
<a href="https://scholar.google.com/citations?user=PXO4ygEAAAAJ&hl=zh-CN">Kun Gai</a>,
<a href="https://scholar.google.com/citations?user=27o9L1wAAAAJ&hl=en">Xianghua Ying</a>,
<a href="https://yisenwang.github.io/">Yisen Wang</a>
</p>
<p align="center">
<a href="http://arxiv.org/abs/2511.21395">
<img src='https://img.shields.io/badge/Paper-PDF-red?style=flat&logo=arXiv&logoColor=red' alt='Paper PDF'>
</a>
<a href="https://huggingface.co/NOVAglow646/Monet-7B" target="_blank" rel="noopener noreferrer">
<img alt="HF Model: ViGaL" src="https://img.shields.io/badge/%F0%9F%A4%97%20_Model-Monet_7B-ffc107?color=ffc107&logoColor=white" height="20" />
</a>
<a href="https://huggingface.co/NOVAglow646/Monet-SFT-7B" target="_blank" rel="noopener noreferrer">
<img alt="HF Model: ViGaL" src="https://img.shields.io/badge/%F0%9F%A4%97%20_Model-Monet_SFT-ffc107?color=ffc107&logoColor=white" height="20" />
</a>
<a href="https://huggingface.co/datasets/NOVAglow646/Monet-SFT-125K" target="_blank">
<img alt="HF Model: ViGaL" src="https://img.shields.io/badge/%F0%9F%A4%97%20_Data-Monet_SFT_125K-ffc107?color=ffc107&logoColor=white" height="20" />
</a>
</p>
</p>
<p align="center">
<img src="images/overview.png" alt="Logo" width="190%">
</p>
We introduce <b>Monet</b>, a training framework that enables multimodal large language models (MLLMs) to reason directly within the latent visual space by generating continuous embeddings that function as intermediate visual thoughts.
<br>
## 🔥Updates
* **2026.3.19** Update `inference/example.sh`: add `export LATENT_SIZE=10`; update `vllm_inference_example.py`: remove the unused `os.environ['LATENT_SIZE'] = '10'`.
* **2026.02.21** **Monet has been accepted by CVPR 2026!🎉**
* **2025.12.30** Upload the models of SFT stage 1/2/3.
* **2025.12.11** Update the prompt in ./inference/vllm_inference_example.py (add "Put your final answer in \\boxed{}.").
* **2025.12.04** Fix typos in RL/examples/vlpo_train.sh
* **2025.12.02** Fix typos in script_examples/sft_stage1.sh, script_examples/sft_stage2.sh, script_examples/sft_stage3.sh
## 🔍Overview
<details open="open" style='padding: 10px; border-radius:5px 30px 30px 5px; border-style: solid; border-width: 1px;'>
<summary>Tabel of Contents</summary>
<ol>
<li>
<a href="#installation">Installation</a>
</li>
<li>
<a href="#training-data">Training Data</a>
</li>
<li>
<a href="#sft-training">SFT Training</a>
</li>
<li>
<a href="#rl-training">RL Training</a>
</li>
<li>
<a href="#inference">Inference</a>
</li>
<li>
<a href="#citation">Citation</a>
</li>
<li>
<a href="#acknowledgement">Acknowledgement</a>
</li>
</ol>
</details>
To support latent reasoning, we use customized Qwen2.5-VL-7B model to replace the official code in Transformers and vLLM.
* [Modified Transformers model (for SFT Training)](./monet_qwen_model/modeling_qwen2_5_vl_monet.py)
* [Modified Transformers model (for RL Training)](./RL/monet_models/transformers)
* [Modified vLLM model (for RL Training)](./RL/monet_models/vllm)
* [Modified vLLM model (for inference)](./inference/vllm/monet_gpu_model_runner.py)
## ⚙Installation
```bash
git clone https://github.com/NOVAglow646/Monet.git
```
SFT environment:
```bash
conda create -n monet python=3.10
conda activate monet
cd Monet
pip install -r requirements.txt
```
RL environment:
```bash
cd Monet/RL
conda create -n easyr1 python=3.11
conda activate easyr1
pip install -r requirements.txt
```
## 📕Training Data
* [SFT data (Monet-SFT-125K)](https://huggingface.co/datasets/NOVAglow646/Monet-SFT-125K/tree/main)
* [RL data (Thyme-RL)](https://huggingface.co/datasets/Kwai-Keye/Thyme-RL)
## 🔧SFT Training
### Training Scripts
See [this folder](./script_examples).
### Implementation Details
The training requires a modification of the official code of Qwen2.5-VL-7B, which is implemented in [this file](./monet_qwen_model/modeling_qwen2_5_vl_monet.py). The main implementation of the forward process with latent embeddings is in `Qwen2_5_VLModel:forward` and `Qwen2_5_VLForConditionalGeneration:forward`.
## 🚀RL Training
We implement our RL training based on [EasyR1](https://github.com/hiyouga/EasyR1).
### Training Scripts
See this [training script](./RL/examples/vlpo_train.sh).
Illustrations of key parameters:
* `worker.rollout.sampling_strategy=monet` Perform latent reasoning during rollout (VLPO is achieved by specifying this parameter); `worker.rollout.sampling_strategy=greedy` Text reasoning.
* `export LATENT_SIZE=10` Number of latent embeddings.
* `worker.rollout.monet.select_acc_threshold=0.6` Select samples with accuracy between $(0, 0.6)$ for training.
* `worker.rollout.online_difficulty_sampling=true` Dynamically sample hard examples for training (with `select_acc_threshold`).
* `worker.actor.monet_rl_sigma=10.0` `worker.ref.monet_rl_sigma` VLPO $\sigma$.
* `worker.reward.repetition_penalty=true` Penalty on repetitive meaningless outputs. Repetition detection is implemented by API.
After training, remember to use [model merging script](./RL/examples/merge_model.sh) to merge the parameter splits and get the final model.
### API Calling
For RL training, we use external LLM APIs (Gemini / DeepSeek) via the helper in `RL/tools/custom_api.py` to support accurate rule-based judgement.
- **Gemini (Google AI)**
- Install the SDK:
```bash
pip install google-genai
```
- Set your API key (from Google AI Studio) before running RL scripts:
```bash
export GOOGLE_API_KEY="<your_gemini_api_key>"
```
- In the [training script](./RL/examples/vlpo_train.sh), use `worker.rule_based_judge.api_name="gemini-2.5-pro"`.
- **DeepSeek**
- Install the OpenAI-compatible SDK:
```bash
pip install openai
```
- Set the API key:
```bash
export DEEPSEEK_API_KEY="<your_deepseek_api_key>"
```
- In the [training script](./RL/examples/vlpo_train.sh), use `worker.rule_based_judge.api_name="deepseek-chat"`.
Please refer to `RL/tools/custom_api.py` for the exact calling interface.
## ⭐Inference
### Download Monet-7B Model
You can download Monet-7B at [this repo](https://huggingface.co/NOVAglow646/Monet-7B). The inference requires replacing the official code of vLLM (see [Modified vLLM model](./monet_qwen_model/vllm/monet_gpu_model_runner.py)).
### Inference Example
See this [quick example](./inference/vllm_inference_example.py) to use Monet-7B with latent reasoning.
* **Setting latent size at inference:** You can control the number of latent embeddings to generate each time the model starts latent reasoning by using: ``` export LATENT_SIZE=10 ```
* **Handling model outputs containing latent reasoning.** To achieve latent-text interleaved reasoning, the model may generate `<abs_vis_token>` to switch to the latent thinking mode. Then, with our [modified vLLM gpu_model_runner.py](./monet_qwen_model/vllm/monet_gpu_model_runner.py), it will replace the next tokens with the representations of the last layer. Since these latent tokens are not human-readable, you can post-process the output by detecting the start token `<abs_vis_token>` and nd replacing the enclosed latent tokens with a clean placeholder such as `<latent>`.
### Evaluation
We evalutate Monet-7B on [VLMEvalKit](https://github.com/open-compass/VLMEvalKit). Notably, we replace the original exact matching judgement with API judge to ensure more accurate assessment.
**How to apply Monet inference in VLMEvalKit:**
- Make sure you use `vllm==0.10.0` for you evaluation environment.
- Clone the VLMEvalKit repo:
```bash
git clone https://github.com/open-compass/VLMEvalKit.git
```
- Copy `monet_gpu_model_runner.py` to a sub-directory of the VLMEvalKit repo:
```bash
mkdir -p xxx/VLMEvalKit/Monet_models
cp xxx/Monet/inference/vllm/monet_gpu_model_runner.py xxx/VLMEvalKit/Monet_models
```
- In `xxx/VLMEvalKit/`, run the following script to create `sitecustomized.py` with the required content:
```bash
cd xxx/VLMEvalKit/
cat > sitecustomized.py <<'PYCODE'
# sitecustomize.py (top-level)
# Runs in every Python process (parent + spawned workers)
import os, sys, importlib
os.environ["VLLM_USE_V1"] = "1" # force V1 engine if desired
os.environ["VLLM_NO_USAGE_STATS"] = "1" # disable usage stats
workspace = os.path.abspath(".")
old_path = os.environ.get("PYTHONPATH", "")
os.environ["PYTHONPATH"] = f"{workspace}:{old_path}" if old_path else workspace
os.environ["LATENT_START_ID"] = "151666"
os.environ["LATENT_END_ID"] = "151667"
sys.modules["vllm.v1.worker.gpu_model_runner"] = importlib.import_module("Monet_models.monet_gpu_model_runner")
PYCODE
```
`sitecustomized.py` will overwrite the corresponding vLLM inference code (`vllm.v1.worker.gpu_model_runner`) with `monet_gpu_model_runner.py` when running codes under the VLMEvalKit directory so that Monet is fulfiled: the logic of `monet_gpu_model_runner.py` is that when model output the start token of latent reasoning (151666), the decoding will be switched to the latent mode.
⚠**Note that:**
To accurately reproduce the result:
* Please use the following system prompt for VLMEvalKit evaluation:
`You are a helpful multimodal assistant. You are required to answer the question based on the image provided. Put your final answer in \\boxed{}.`
* Please apply an API model as a supplementary judge.
## 🖊Citation
If you find this work useful, please use the following BibTeX. Thank you for your support!
```bibtex
@inproceedings{wang2025monetreasoninglatentvisual,
title={Monet: Reasoning in Latent Visual Space Beyond Images and Language},
author={Qixun Wang and Yang Shi and Yifei Wang and Yuanxing Zhang and Pengfei Wan and Kun Gai and Xianghua Ying and Yisen Wang},
year={2026},
booktitle={CVPR}
}
```
## 🙏Acknowledgement
We sincerely thank the following great works as they provide valuable data or code for our work:
* [Zebra-CoT](https://huggingface.co/datasets/multimodal-reasoning-lab/Zebra-CoT)
* [Visual-CoT](https://huggingface.co/datasets/deepcs233/Visual-CoT)
* [CogCoM](https://github.com/zai-org/CogCoM)
* [ReFoCus](https://arxiv.org/abs/2501.05452)
* [EasyR1](https://github.com/hiyouga/EasyR1)
* [VLMEvalKit](https://github.com/open-compass/VLMEvalKit)
* [Mirage](https://github.com/UMass-Embodied-AGI/Mirage)