MM-EUREKA

📖Paper | 📊Datasets | 🤗MM-Eureka-8B | 🤗MM-Eureka-Zero-38B

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MM-EUREKA: Exploring Visual Aha Moment with Rule-based Large-scale Reinforcement Learning

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[[Paper PDF Link]]

🎯Overview

We present MM-Eureka and MM-Eureka-Zero, a series of multimodal reasoning models that successfully extend large-scale rule-based reinforcement learning (RL) to multimodal reasoning.

While rule-based RL has shown remarkable success in improving LLMs' reasoning abilities in text domains, its application to multimodal settings has remained challenging. Our work reproduces key characteristics of text-based RL systems like DeepSeek-R1 in the multimodal space for the first time, including steady increases in accuracy reward and response length, and the emergence of reflection behaviors.

We demonstrate that both instruction-tuned and pre-trained models can develop strong multimodal reasoning capabilities through rule-based RL without supervised fine-tuning, showing superior data efficiency compared to alternative approaches.

🔥We open-source our complete pipeline to foster further research in this area. We release all our codes, models, data, etc. at MM-EUREKA

🗞️ News

• [2025/03/07] We released MM-Eureka.
  • 📖 Paper: MM-Eureka-paper
  • 🤗 Model: MM-Eureka-8B & MM-Eureka-Zero-38B
  • 📊 Dataset: MM-Eureka-Dataset

🚀 Features

This repository is built upon OpenRLHF, introducing several key enhancements:

• Multimodal RFT Support: Extends OpenRLHF to incorporate vision-language models (VLMs), currently supporting InternVL, enabling multimodal reasoning capabilities.
  • Currently support RLOO, REINFORCE++, GRPO training using Ray.
  • vLLM integration and distributed training.
  • Support hybrid engine (--colocate_all_models, --vllm_enable_sleep).
• Better Rule-based Reward support: Better training visualization for Rule-based Rewards (i.g. Format Reward, Accuracy Reward, Repetition Penalty)
• Online Filtering: Filtering out experiences based on Accuracy Reward during training as in PRIME
  • Use --enable_accuracy_filter, --freezing_filter_steps, --accuracy_lower_bound, --accuracy_upper_bound to control the behavior of online accuracy filter.
  • Online Accuracy filter is not currently enabled in our default settings, refer to the Disccusion Section in our paper for more details.

🤖 Models

*Figure 1 | Train Time Scale-up on Accuracy Reward and Response Length of Rule-Based RL. (a) represents the training scenario on InternVL2.5-instruct-8B, while (b) corresponds to the training scenario on InternVL2.5-pretrained-38B. It can be observed that stable improvements in accuracy reward and response length can be achieved regardless of whether the model is based on an instruct model or a pretrained model.*

• 🤗 MM-Eureka-8B

• 🤗 MM-Eureka-Zero-38B

🏁 Getting Started

📦 Installation

git clone https://github.com/ModalMinds/MM-EUREKA.git
cd MM-EUREKA
pip install -e .[vllm]

# install flash-attn==2.3.6:

pip install flash-attn==2.3.6 --no-build-isolation

# Alternatively you can compile from source:

git clone https://github.com/Dao-AILab/flash-attention.git
cd flash-attention
git checkout v2.3.6
python setup.py install

📂 Data Preparation

You can download our training data from MM-Eureka-Dataset

Once downloaded, refer to the section below for additional data formation. You may need to update the image_urls field to reference your local image paths for proper processing.

Custom dataset

For custom dataset, format your data in to a JSONL file, where each entry is a dictionary organized in the following format.

{
  "id": "0",
  "conversations": [
      {
          "role": "system",
          "content": "system_prompt"
      },
      {
          "role": "user",
          "content": "user_prompt"
      }
  ],
  "answer": "gt that could be parsed and verified by math_verify",
  "image_urls": ["file:///path/to/image1", "file:///path/to/image2"]
}

Note

For text-only inputs, we follow InternVL's official approach, which requires a dummy image input. Specifically, you should provide a (224, 224) pure white image as a placeholder. We have already provided such a blank image at: examples/blank.png

🌐 Start Training

Before starting your own training, ensure that the paths in the provided training scripts are correctly set and that environment variables like $MASTER_ADDR and $NODE_RANK are properly configured.

start MM-Eureka-8B training

• for single node

  sh examples/scripts/train_mm_eureka_8b_single_node.sh

• for multiple node

  sh examples/scripts/train_mm_eureka_8b_multi_node.sh

start MM-Eureka-Zero-38B training

sh examples/scripts/train_mm_eureka_zero_38b_multi_node.sh

📊 Evaluation

We provide our evaluation code in the eval/ directory. To customize the evaluation process for different models, use the --prompt_template argument to specify the appropriate prompt format.

We also introduce the K12 Math Dataset, a curated set of 500 fill-in-the-blank mathematics questions covering concepts from middle to high school levels. The dataset can be found in the eval/k12 directory

⭐ Starchart

🤝 Contribution

MM-Eureka is stil under active development, if you want to contribute, please feel free to make a pull request or create an issue.

Please refer to CONTRIBUTING.md before you dive in！

📬 Contact

If you have any questions or would like to engage with our community, feel free to scan the QR code below to join our WeChat group.

🎓 Acknowledgements

We acknowledge the outstanding open-source contributions from OpenRLHF, LMM-R1 and vLLM. We also extend our gratitude to DeepSeek-R1 and InternVL for their open-source techniques and base models, which have enabled us to further our exploration.

📜 Citation

@misc{MM-EUREKA2025,
  title={MM-EUREKA: Exploring Visual Aha Moment with Rule-Based Large-Scale Reinforcement Learning},
  author={Fanqing Meng and Lingxiao Du and Zongkai Liu and Zhixiang Zhou and Quanfeng Lu and Daocheng Fu and Botian Shi and Wenhai Wang and Junjun He and Kaipeng Zhang and Ping Luo and Yu Qiao and Qiaosheng Zhang and Wenqi Shao},
  year={2025},
  howpublished={\url{https://github.com/ModalMinds/MM-EUREKA}},
}