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Recently, the Natural Language Intelligence Team of the Tongyi Lab officially released and open-sourced VRAG-RL — a multi-modal RAG reasoning framework driven by visual perception. It aims to solve the problem of how AI can retrieve key information and conduct fine-grained reasoning from visual languages such as images, tables, design drafts, etc., in real-world business scenarios. Retrieving and reasoning about key information in complex visual document knowledge bases has always been a major challenge in the AI field. Traditional retrieval-augmented generation (RAG) methods struggle when handling visually rich information because they find it difficult to...

A recent paper jointly published by Tsinghua University and Shanghai Jiao Tong University challenges the widely held belief that pure reinforcement learning (RL) enhances large model reasoning capabilities. The research found that models incorporating reinforcement learning performed worse than their original counterparts in certain tasks.

Recently, a collaborative effort between the Institute of Automation, Chinese Academy of Sciences, and the CASIA-Zidong Taichu team introduced Vision-R1, a novel method leveraging R1-like reinforcement learning to significantly enhance visual localization capabilities. This approach achieved a 50% performance improvement in complex tasks such as object detection and visual localization, surpassing existing state-of-the-art (SOTA) models with over ten times the parameter scale. Currently, large vision-language models typically rely on "pre-training + supervised fine-tuning" to improve responsiveness to user instructions, but this...