VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich
  Information Understanding via Iterative Reasoning with Reinforcement Learning
Effectively retrieving, reasoning and understanding visually rich information
remains a challenge for RAG methods. Traditional text-based methods cannot
handle visual-related information. On the other hand, current vision-based RAG
approaches are often limited by fixed pipelines and frequently struggle to
reason effectively due to the insufficient activation of the fundamental
capabilities of models. As RL has been proven to be beneficial for model
reasoning, we introduce VRAG-RL, a novel RL framework tailored for complex
reasoning across visually rich information. With this framework, VLMs interact
with search engines, autonomously sampling single-turn or multi-turn reasoning
trajectories with the help of visual perception tokens and undergoing continual
optimization based on these samples. Our approach highlights key limitations of
RL in RAG domains: (i) Prior Multi-modal RAG approaches tend to merely
incorporate images into the context, leading to insufficient reasoning token
allocation and neglecting visual-specific perception; and (ii) When models
interact with search engines, their queries often fail to retrieve relevant
information due to the inability to articulate requirements, thereby leading to
suboptimal performance. To address these challenges, we define an action space
tailored for visually rich inputs, with actions including cropping and scaling,
allowing the model to gather information from a coarse-to-fine perspective.
Furthermore, to bridge the gap between users' original inquiries and the
retriever, we employ a simple yet effective reward that integrates query
rewriting and retrieval performance with a model-based reward. Our VRAG-RL
optimizes VLMs for RAG tasks using specially designed RL strategies, aligning
the model with real-world applications. The code is available at
https://github.com/Alibaba-NLP/VRAG{https://github.com/Alibaba-NLP/VRAG}.
6837ed297d00cf0a04677bc1
Qiuchen Wang
claimed_verified
2025-05-29T09:40:19.039Z
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QiuchenWang
user
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6837ed297d00cf0a04677bc2
Ruixue Ding
6837ed297d00cf0a04677bc3
Yu Zeng
claimed_verified
2025-05-29T07:41:42.906Z
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Yu Zeng
user
YuZeng260
6837ed297d00cf0a04677bc4
Zehui Chen
6837ed297d00cf0a04677bc5
Lin Chen
claimed_verified
2025-05-29T07:41:44.983Z
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Lin Chen
user
Lin-Chen
6837ed297d00cf0a04677bc6
Shihang Wang
6837ed297d00cf0a04677bc7
Pengjun Xie
6837ed297d00cf0a04677bc8
Fei Huang
6837ed297d00cf0a04677bc9
Feng Zhao
VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich
  Information Understanding via Iterative Reasoning with Reinforcement Learning
Effectively retrieving, reasoning and understanding visually rich information
remains a challenge for RAG methods. Traditional text-based methods cannot
handle visual-related information. On the other hand, current vision-based RAG
approaches are often limited by fixed pipelines and frequently struggle to
reason effectively due to the insufficient activation of the fundamental
capabilities of models. As RL has been proven to be beneficial for model
reasoning, we introduce VRAG-RL, a novel RL framework tailored for complex
reasoning across visually rich information. With this framework, VLMs interact
with search engines, autonomously sampling single-turn or multi-turn reasoning
trajectories with the help of visual perception tokens and undergoing continual
optimization based on these samples. Our approach highlights key limitations of
RL in RAG domains: (i) Prior Multi-modal RAG approaches tend to merely
incorporate images into the context, leading to insufficient reasoning token
allocation and neglecting visual-specific perception; and (ii) When models
interact with search engines, their queries often fail to retrieve relevant
information due to the inability to articulate requirements, thereby leading to
suboptimal performance. To address these challenges, we define an action space
tailored for visually rich inputs, with actions including cropping and scaling,
allowing the model to gather information from a coarse-to-fine perspective.
Furthermore, to bridge the gap between users' original inquiries and the
retriever, we employ a simple yet effective reward that integrates query
rewriting and retrieval performance with a model-based reward. Our VRAG-RL
optimizes VLMs for RAG tasks using specially designed RL strategies, aligning
the model with real-world applications. The code is available at
https://github.com/Alibaba-NLP/VRAG{https://github.com/Alibaba-NLP/VRAG}.
VRAG-RL, a reinforcement learning framework, enhances reasoning and visual information handling in RAG methods by integrating visual perception tokens and employing specialized action spaces and rewards.
reinforcement learning VRAG-RL VLMs search engines visually rich information reasoning trajectories visual perception tokens action space query rewriting retrieval performance model-based reward