VRAG-RL - A multimodal RAG inference framework launched by Alitongyi
VRAG-RL is a vision-perception-driven multimodal RAG inference framework developed by Alibaba's Tongyi Big Model team. It focuses on improving the retrieval, reasoning, and understanding capabilities of visual language models (VLMs) when processing rich visual information. ...
What is VRAG-RL?
VRAG-RL, developed by Alibaba's Tongyi Big Model team, is a vision-perception-driven multimodal RAG inference framework. It focuses on enhancing the retrieval, reasoning, and understanding capabilities of Visual Language Models (VLMs) when processing rich visual information. Based on a defined visual perception action space, it allows the model to progressively acquire information from coarse-grained to fine-grained levels, more effectively activating its reasoning ability. VRAG-RL introduces a comprehensive reward mechanism, combining retrieval efficiency with model-based reward outcomes to optimize the model's retrieval and generation capabilities. In multiple benchmark tests, VRAG-RL significantly outperforms existing methods, demonstrating its powerful potential in the field of understanding rich visual information.
Main functions of VRAG-RL
- Enhanced visual perceptionBased on the defined visual perception action space (such as cropping and scaling), the model can gradually acquire information from coarse to fine granular, thus more effectively activating the model's reasoning ability.
- Multi-turn interactive reasoningIt supports multi-turn interactions, allowing the model to continuously interact with the search engine and gradually optimize the inference process.
- Comprehensive reward mechanismBy combining retrieval efficiency and model-based result rewards, we can comprehensively guide the model to optimize its reasoning and retrieval capabilities, making the model more closely aligned with real-world application scenarios.
- ScalabilityThe framework has good scalability, supports the integration of various tools and models, and is convenient for users to customize and extend.
The technical principle of VRAG-RL
- Visual perception of motion spaceDefine a set of visual perception actions, including selecting regions of interest, cropping, and scaling. These actions allow the model to progressively acquire information from coarse to fine granular levels, more effectively focusing on information-dense regions.
- Reinforcement learning framework: Optimize the model's inference and retrieval capabilities using reinforcement learning (RL). Based on interaction with the search engine, the model autonomously samples single or multiple rounds of inference trajectories and continuously optimizes based on the samples.
- Comprehensive reward mechanismDesign a comprehensive reward function that includes a retrieval efficiency reward, a pattern consistency reward, and a model-based result reward. The reward mechanism focuses on the final result, optimizing the retrieval process and enabling the model to acquire relevant information more effectively.
- Multi-round interactive trainingBased on a multi-round interactive training strategy, the model gradually optimizes the inference process through continuous interaction with the external environment, thereby improving the stability and consistency of inference.
- Data augmentation and pre-trainingThe training data is expanded based on a multi-expert sampling strategy to ensure that the model learns effective visual perception and reasoning capabilities during the pre-training phase.
VRAG-RL project address
- GitHub repository:https://github.com/Alibaba-NLP/VRAG
- HuggingFace model library:https://huggingface.co/collections/autumncc/vrag-rl
- arXiv technical paper:https://arxiv.org/pdf/2505.22019
Application scenarios of VRAG-RL
- Intelligent document Q&AQuickly retrieve and understand information from PPTs, reports, and other documents to answer questions efficiently.
- Visual Information RetrievalQuickly locate and extract relevant visual information from a large number of charts and images.
- Multimodal content generationIt combines visual and textual information to generate summaries and reports that are rich in graphics and text.
- Education and Training: To assist teaching and help students better understand and analyze visual materials.
- Intelligent customer service and virtual assistant: Handle user questions involving visual content and provide accurate answers.