This repository serves as a curated collection of research and projects focused on enhancing Multimodal Large Language Model (MLLM) reasoning capabilities through Reinforcement Learning (RL). It targets researchers and practitioners in the field of multimodality, providing a comprehensive overview of recent advancements, papers, code, and datasets in RL-based MLLM reasoning.

How It Works

The repository highlights the growing trend of using RL techniques to improve the reasoning abilities of MLLMs, drawing parallels to advancements in text-based LLMs. It categorizes research by modality (vision, video, audio, etc.) and application areas (GUI agents, metaverse), showcasing how RL is applied to imbue these models with more sophisticated cognitive processes, such as chain-of-thought, self-reflection, and spatial understanding.

Quick Start & Requirements

This repository is a curated list and does not have a direct installation or execution command. Users are directed to individual project repositories linked within the README for specific setup and requirements.

Highlighted Details

• Comprehensive listing of recent papers (sorted by release date) and associated code/models across various modalities.
• Includes dedicated sections for benchmarks and open-source training frameworks relevant to RL-based MLLM reasoning.
• Features projects exploring diverse applications, from medical image analysis to GUI agents and metaverse environments.
• Provides links to demos, datasets, and project pages for deeper exploration of individual works.

Maintenance & Community

The repository is actively maintained, with recent entries dated April 2025. Contributions are welcomed, and contact information for the primary author is provided.

Licensing & Compatibility

The repository itself is licensed under the MIT License. Individual projects linked within the repository will have their own licenses, which users must consult for compatibility and usage restrictions.

Limitations & Caveats

This is a curated list of research and does not provide a unified framework or tool. Users must navigate to individual project repositories to access code, models, and understand specific implementation details and potential limitations.