ViRC-3B

<div align="center"> <img src="docs/logo.png" alt="logo" height="150"> <h1 style="font-size: 32px; font-weight: bold;"> [CVPR 2026] VɪRC: Enhancing Visual Interleaved Mathematical CoT with Reason Chunking </h1>

<a href="https://arxiv.org/abs/2512.14654v3"><img src="https://img.shields.io/badge/ArXiv-ViRC-brown?logo=arxiv" alt="Paper"></a> <a href="https://huggingface.co/datasets/LeonMiao/CRUX"><img src="https://img.shields.io/badge/🤗 huggingface-CRUX-blue" alt="dataset"></a> <br> <a href="https://huggingface.co/LeonMiao/ViRC-7B"><img src="https://img.shields.io/badge/🤗 huggingface-ViRC--7B-purple" alt="checkpoint"></a> <a href="https://huggingface.co/LeonMiao/ViRC-3B"><img src="https://img.shields.io/badge/🤗 huggingface-ViRC--3B-purple" alt="checkpoint"></a> <a href="https://huggingface.co/LeonMiao/ViRC-Qwen2VL-7B"><img src="https://img.shields.io/badge/🤗 huggingface-ViRC--Qwen2VL--7B-purple" alt="checkpoint"></a> <a href="https://huggingface.co/LeonMiao/ViRC-Qwen2VL-2B"><img src="https://img.shields.io/badge/🤗 huggingface-ViRC--Qwen2VL--2B-purple" alt="checkpoint"></a>

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💥 News

• [2026.04.09] We officially open-source the CRUX dataset and ViRC models as scheduled. 🚀
• [2026.03.01] The CURX dataset and ViRC models are ready and are currently under internal review at Ant Group. Full open-sourcing will be completed no later than 2026.04.19.⚡️
• [2026.02.21] ViRC has been accepted by CVPR 2026. 🎉
• [2025.12.16] We release the arxiv paper and the code. 🔥

👀 About VɪRC

Existing MLLMs typically perform textual reasoning solely from a single static mathematical image, overlooking dynamic visual acquisition during reasoning. In contrast, humans repeatedly examine visual image and employ step-by-step reasoning to prove intermediate propositions. We propose a ViRC framework for multimodal mathematical tasks, introducing a Reason Chunking mechanism that structures multimodal mathematical CoT into consecutive Critical Reasoning Units (CRUs) to simulate human expert problem-solving patterns. CRUs ensure intra-unit textual coherence for intermediate proposition verification while integrating visual information across units to generate subsequent propositions and support structured reasoning.

<p align="center"> <img src="docs/teaser.png" width="40%"> <br> </p>

To this end, we present CRUX dataset by using three visual tools and four reasoning patterns to provide explicitly annotated CRUs across multiple reasoning paths for each mathematical problem.

<p align="center"> <img src="docs/data_pipeline.png"> <br> </p>

Leveraging the CRUX dataset, we propose a progressive training strategy inspired by human cognitive learning, which includes Instructional SFT, Practice SFT, and Strategic RL, aimed at further strengthening the Reason Chunking ability of the model.

<p align="center"> <img src="docs/training_strategy.png" width="40%"> <br> </p>

The resulting ViRC-7B model achieves a 18.8% average improvement over baselines across multiple mathematical benchmarks and cross-domain high-resolution image benchmarks.

💪 Get Started

Installation

Clone the repository:

git clone https://github.com/Leon-LihongWang/ViRC.git
cd ViRC

Create and activate a conda environment:

conda create -n virc python=3.10 -y
conda activate virc

Install additional dependencies:

bash src/setup.sh

🚀 Training

Preparation

Download our dataset, and extract ViRC_images.tar.lz4:

huggingface-cli repo download --repo-type dataset LeonMiao/CRUX --local-dir ./data
cd ./data && lz4 -d ViRC_images.tar.lz4 | tar -xf -

Download Qwen2.5-VL-7B-Instruct, which is the base model used for training.

huggingface-cli repo download --repo-type model Qwen/Qwen2.5-VL-7B-Instruct --local-dir ./model

Stage 1: Instructional SFT

cd ViRC
pip install datasets==3.6.0
DISABLE_VERSION_CHECK=1 llamafactory-cli train src/train/stage_1_InstrSFT.yaml

Stage 2: Practice SFT

DISABLE_VERSION_CHECK=1 llamafactory-cli train src/train/stage_2_PracSFT.yaml

Stage 3: Strategic RL

pip install datasets==4.0.0
# Start Qwen2.5-VL-72B-Instruct with vLLM, and update Lines 32, 36, and 37 accordingly (the model-related settings).
\cp -f src/train/reward_for_scale_dynamic.py ./verl/utils/reward_score/init.py
bash src/train/stage_3_StratRL

Notes on Training Config

• We provide src/train/merge_rl_result.sh to merge Strategic RL outputs and export the final model weights in safetensors format.
• Our training data and scripts support two image-resolution settings:
  • Dynamic resolution (for models such as Qwen2.5VL).
  • Fixed resolution (images resized to 1000×1000, for models such as Qwen2VL/Qwen3VL).
    These are distinguished by the suffixes _scale_dynamic and _scale_fixed under data/ and src/.
• For reproducibility, we release a 50K subset of the training data (suffix _50K) and the full 100K set (suffix _100K).

💫 Inference

We provide inference scripts for two image-resolution settings:

• Dynamic resolution (src/evaluation/ViRC_scale_dynamic.py): for ViRC-7B and ViRC-3B, based on the Qwen2.5-VL-Instruct series.
• Fixed resolution (1000×1000) (src/evaluation/ViRC_scale_fixed.py): for ViRC-Qwen2VL-7B and ViRC-Qwen2VL-2B, based on the Qwen2-VL-Instruct series.

src/evaluation/ also includes a sample input image (image.png) and an expected output example in src/evaluation/response/ for a quick sanity check.

Start the ViRC model with vLLM, then run the evaluation script:

model_path=./ViRC/models/ViRC-7B
model_name=ViRC
tensor_parallel_size=4
port=8000

python -u -m vllm.entrypoints.openai.api_server \
  --model $model_path \
  --served-model-name $model_name \
  --dtype auto \
  --tensor-parallel-size $tensor_parallel_size \
  --gpu-memory-utilization 0.9 \
  --port $port

python src/evaluation/ViRC_scale_dynamic.py

🥳 Acknowledgements

We would like to thank LLaMA-Factory and verl, upon which our repo is built.

✅ Citation

@article{wang2025virc,
  title={ViRC: Enhancing Visual Interleaved Mathematical CoT with Reason Chunking}, 
  author={Lihong, Wang and Liangqi, Li and Weiwei, Feng and Jiamin, Wu and Changtao, Miao and Tieru, Wu and Rui, Ma and Bo, Zhang and Zhe, Li},
  journal={arXiv preprint arXiv:2512.14654},
  year={2025}
}