DoGe: Decouple to Generalize 🚀

Overview 🔍

DoGe (Decouple to Generalize) is a dual-decoupling reinforcement learning framework designed to enable self-evolving learning for vision-language models (VLMs) in data-scarce specialized domains (e.g., chemistry, earth science, multimodal mathematics).

Core Challenge Addressed

Traditional RL-based VLM training suffers from:

• Lack of high-quality multimodal data in specialized domains
• Reward hacking (models exploit high-reward shortcuts instead of genuine reasoning)
• Entropy collapse and poor generalization

Key Innovation

DoGe restructures the model's cognitive process into a "learning-application" cycle by decoupling the policy into two complementary components:

1. 🤔 Thinker: Learns to deeply understand contextual information (without explicit questions) through free exploration
2. 🧩 Solver: Uses the Thinker's analysis to solve original tasks, providing quantitative rewards for the Thinker

Training Pipeline

The framework adopts a two-stage RL training loop aligned with human cognitive logic:

1. Stage 1 (Learning from Context): Train Thinker to analyze question-masked multimodal context; Solver's accuracy quantifies Thinker's performance
2. Stage 2 (Learning from Application): Fine-tune Thinker on original tasks to internalize reasoning capabilities via GRPO annealing

Data Synthesis

DoGe builds an iterative curriculum learning pipeline:

• 🌐 Multimodal Knowledge Pool: Aggregates unlabeled domain data (images + text) from web/databases
• 🔄 Seed Problem Pool: Dynamically updates with "occasionally solvable" problems to enhance data diversity

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Quick Start

🖥️ Environment Setup

Configure the environment according to the guidelines below：

# Example: Create and activate a virtual environment (replace with actual commands)
conda create -n doge python=3.10
conda activate doge
# clone this repository
git clone https://github.com/opendatalab-raiser/DoGe
cd DoGe
# Install dependency packages
pip install -r requirements.txt

📥 Dataset Download

You can directly download datasets from our official Huggingface Repository DoGe:

# Create a dedicated directory for storing the dataset
mkdir -p data
# Clone the dataset repository from Hugging Face to the data directory
git clone https://huggingface.co/datasets/opendatalab-raiser/DoGe data/DoGe
cd data/DoGe
# Unzip the image archive file
tar -xzf imgs.tar.gz

▶️ Run Experiment

Replace the corresponding parameters in the startup file, including the dataset and model, with your actual paths:

# DoGe Training Stage 1: Thinker
bash scripts/run_qwen2_5_vl-7b_doge.sh
# DoGe Training Stage 2: Anneal
bash scripts/run_qwen2_5_vl-7b.sh

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Experiment Results 📊

We evaluate DoGe on 7 benchmarks covering:

• General visual reasoning & hallucination (MMMU, MMStar, HallBench)
• Specialized domain reasoning (MathVision, MathVista, ChemBench, MSEarthMCQ)

3B-level Models Performance

Method	MMMU	MMStar	HallBench	MathVision	MathVista	ChemBench	MSEarthMCQ	Avg.
InternVL2.5-2B	43.6	53.7	42.6	13.5	51.3	-	-	-
Visionary-3B	40.7	50.5	59.8	17.1	54.7	40.8	38.2	43.1
Qwen2.5VL-3B* (Base)	41.0	49.3	60.6	18.7	48.8	43.4	40.8	43.2
DoGe-3B (Iter1)	46.6	54.5	61.5	21.7	🥇57.9	45.8	🥇48.3	48.0
DoGe-3B (Iter2)	48.9	52.5	🥇62.5	23.1	54.2	🥇47.7	46.2	47.9
DoGe-3B (Iter3)	🥇50.2	🥇54.7	61.8	🥇24.2	57.0	46.9	47.3	🥇48.9
⬆️ Max Gain (vs. Base)	+9.2	+5.4	+1.9	+5.5	+9.1	+4.3	+7.5	+5.7

7B-level Models Performance

Method	MMMU	MMStar	HallBench	MathVision	MathVista	ChemBench	MSEarthMCQ	Avg.
InternVL2.5-8B	48.9	62.8	50.1	22.0	64.4	-	-	-
Vision-R1-7B	46.9	60.8	66.7	🥇29.0	68.5	46.0	44.1	51.7
Qwen2.5VL-7B* (Base)	49.9	60.7	66.3	23.6	64.1	48.6	43.3	50.9
DoGe-7B (Iter1)	53.1	🥇63.2	54.4	24.3	62.1	48.7	46.4	50.3
DoGe-7B (Iter2)	50.9	60.0	🥇68.3	25.3	🥇68.8	🥇49.0	🥇46.5	52.7
DoGe-7B (Iter3)	🥇53.6	63.0	68.0	25.2	68.3	48.5	45.8	🥇53.2
⬆️ Max Gain (vs. Base)	+3.7	+2.5	+2.0	+1.7	+4.7	+0.4	+3.2	+2.3

Key Takeaways ✨

1. Stable Self-Evolution: DoGe achieves consistent performance improvement across 3 iterations for both 3B and 7B models
2. Domain Generalization:
   • 3B models: Average +5.7% performance gain across all benchmarks
   • 7B models: Average +2.3% performance gain (maintains superiority over strong baselines)
3. Hallucination Reduction: +2.0% average improvement on HallBench, mitigating visual hallucination
4. Data Efficiency: Excels in data-scarce domains (Chemistry, Earth Science) with limited manual annotations

Visualization Highlights

• 📈 Higher policy entropy throughout training (avoids entropy collapse)
• 🌐 Wider distribution of synthetic training data compared to manual annotations
• 🔄 Stable performance across iterations (unlike baseline's fluctuating results)

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🙏 Acknowledgements

The code implementation of our work is based on verl, and we would like to express our gratitude to this project for providing an excellent VLM reinforcement learning toolkit.

✍️ Citation

@misc{li2025decouplegeneralizecontextfirstselfevolving,
title={Decouple to Generalize: Context-First Self-Evolving Learning for Data-Scarce Vision-Language Reasoning}, author={Tingyu Li and Zheng Sun and Jingxuan Wei and Siyuan Li and Conghui He and Lijun Wu and Cheng Tan},
year={2025},
eprint={2512.06835},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2512.06835}, }