# Reinforcement Learning for Multimodal Foundation Models: A Comprehensive Collection of Research Resources

> This systematically organizes cutting-edge research on applying reinforcement learning (RL) to multimodal large models, covering the latest advances in visual-language models, visual generation, embodied intelligence, and other directions.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-04-28T12:15:17.000Z
- 最近活动: 2026-04-28T12:21:34.211Z
- 热度: 150.9
- 关键词: 强化学习, 多模态, 大语言模型, 视觉理解, 图像思维, 具身智能, 综述, MLLM
- 页面链接: https://www.zingnex.cn/en/forum/thread/llm-github-weijiawu-awesome-rl-for-multimodal-foundation-models
- Canonical: https://www.zingnex.cn/forum/thread/llm-github-weijiawu-awesome-rl-for-multimodal-foundation-models
- Markdown 来源: floors_fallback

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## Introduction: Collection of Research Resources on Multimodal Foundation Models and Reinforcement Learning

This article introduces the Awesome-RL-for-Multimodal-Foundation-Models project, which systematically organizes cutting-edge research on applying reinforcement learning (RL) to multimodal large models (MLLMs), covering visual-language models, visual generation, embodied intelligence, and other directions. Through a structured classification system, the project provides resource navigation for researchers, helping them quickly locate research directions of interest.

## Background: The Rise of Combining Multimodal Large Models with RL and Project Positioning

As the capabilities of MLLMs evolve rapidly, enhancing their visual understanding, reasoning, and decision-making abilities has become a common focus of academia and industry. RL, as a machine learning method that optimizes strategies through environmental interaction, injects vitality into the development of multimodal models. The Awesome-RL-for-Multimodal-Foundation-Models project is a carefully curated collection of papers and code, focusing on the intersection of visual RL. Its target audience includes researchers in RL, computer vision, and other fields, and it helps users track progress through structured classification.

## Methodology: Domain Classification System and Evolution of Technical Routes

The project organizes research using a hierarchical structure, including directions such as multimodal LLM and RL (e.g., GDPO, CapRL), perception-centric research (e.g., SVQA-R1, UniVG-R1), image thinking (e.g., VisionThink, GRIT), video understanding (e.g., Video-MTR), and visual generation (e.g., ImageReward). The evolution of technical routes is reflected in: refined reward design (e.g., process reasoning rewards), visualization of chain reasoning (generating intermediate visual states), and integration of tool use with RL (e.g., THOR).

## Evidence: Representative Research Achievements and Academic Impact

Representative works in each direction include: GDPO and CapRL for multimodal LLM and RL; VisionThink and Pixel Reasoner for image thinking; ImageReward for visual generation, etc. The survey paper associated with the project, "Reinforcement Learning for Large Model: A Survey", is the first comprehensive review in this field, establishing the "RL for Large Model" paradigm. The project includes the latest achievements from 2023 to 2026, reflecting the activity of the field.

## Application Scenarios: Diverse Applications of RL in Multimodal Fields

Application scenarios of RL in multimodal fields include: robotics and embodied intelligence (learning control strategies from visual inputs), interactive environments (game/simulation decision-making), document understanding (DocR1 optimizing multi-page document comprehension), chart reasoning (BigCharts-R1 processing structured visual content), and anomaly detection (VAU-R1 applied to video anomaly understanding).

## Significance: Core Value for Researchers

The significance of this project for researchers includes: 1. A clear research map (understanding the overall picture through the classification system); 2. Tracking cutting-edge progress (accessing the latest papers and code); 3. Inspiring research directions (discovering opportunities through representative works); 4. Resource aggregation (improving research efficiency).

## Outlook: Future Directions of Combining Multimodal Models with RL

The combination of multimodal foundation models and RL is in a period of rapid development. With the expansion of model scale and improvement of computing power, more breakthrough applications are expected to emerge. In particular, the image thinking paradigm may completely change multimodal reasoning and understanding. The continuous maintenance of the project will provide important infrastructure support for this field.