[CVPR 2026] Monet: Reasoning in Latent Visual Space Beyond Images and Language

Qixun Wang, Yang Shi, Yifei Wang, Yuanxing Zhang, Pengfei Wan, Kun Gai, Xianghua Ying, Yisen Wang

Paper PDF HF Model: ViGaL HF Model: ViGaL HF Model: ViGaL

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We introduce Monet, 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.
## 🔥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
Tabel of Contents
  1. Installation
  2. Training Data
  3. SFT Training
  4. RL Training
  5. Inference
  6. Citation
  7. Acknowledgement
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="" ``` - 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="" ``` - 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 `` 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 `` and nd replacing the enclosed latent tokens with a clean placeholder such as ``. ### 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)