title: UniVG-R1: Reasoning Guided Universal Visual Grounding with Reinforcement
    Learning
summary: Traditional visual grounding methods primarily focus on single-image
  scenarios with simple textual references. However, extending these methods to
  real-world scenarios that involve implicit and complex instructions,
  particularly in conjunction with multiple images, poses significant challenges,
  which is mainly due to the lack of advanced reasoning ability across diverse
  multi-modal contexts. In this work, we aim to address the more practical
  universal grounding task, and propose UniVG-R1, a reasoning guided multimodal
  large language model (MLLM) for universal visual grounding, which enhances
  reasoning capabilities through reinforcement learning (RL) combined with
  cold-start data. Specifically, we first construct a high-quality
  Chain-of-Thought (CoT) grounding dataset, annotated with detailed reasoning
  chains, to guide the model towards correct reasoning paths via supervised
  fine-tuning. Subsequently, we perform rule-based reinforcement learning to
  encourage the model to identify correct reasoning chains, thereby incentivizing
  its reasoning capabilities. In addition, we identify a difficulty bias arising
  from the prevalence of easy samples as RL training progresses, and we propose a
  difficulty-aware weight adjustment strategy to further strengthen the
  performance. Experimental results demonstrate the effectiveness of UniVG-R1,
  which achieves state-of-the-art performance on MIG-Bench with a 9.1%
  improvement over the previous method. Furthermore, our model exhibits strong
  generalizability, achieving an average improvement of 23.4% in zero-shot
  performance across four image and video reasoning grounding benchmarks. The
  project page can be accessed at https://amap-ml.github.io/UniVG-R1-page/.
paper_authors._id: 682db25d265177367e35d5b1
paper_authors.name: Sule Bai
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-28T09:01:23.476Z
paper_authors._id: 682d81e389b5f79a1d7b9682
paper_authors.avatarUrl: /avatars/4e0b4f1654de8c74da5b54b154de3e65.svg
paper_authors.fullname: SuleBai
paper_authors.type: user
paper_authors.user: SuleBai
paper_authors._id: 682db25d265177367e35d5b2
paper_authors.name: Mingxing Li
paper_authors._id: 682db25d265177367e35d5b3
paper_authors.name: Yong Liu
paper_authors._id: 682db25d265177367e35d5b4
paper_authors.name: Jing Tang
paper_authors._id: 682db25d265177367e35d5b5
paper_authors.name: Haoji Zhang
paper_authors._id: 682db25d265177367e35d5b6
paper_authors.name: Lei Sun
paper_authors._id: 682db25d265177367e35d5b7
paper_authors.name: Xiangxiang Chu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-21T12:27:07.959Z
paper_authors._id: 66d255e3947594430c723ff6
paper_authors.avatarUrl: /avatars/c56e4792332a01bf34085a75ee64916e.svg
paper_authors.fullname: xiaochonglinghu
paper_authors.type: user
paper_authors.user: xiaochonglinghu
paper_authors._id: 682db25d265177367e35d5b8
paper_authors.name: Yansong Tang
paper_title: UniVG-R1: Reasoning Guided Universal Visual Grounding with Reinforcement
    Learning
paper_summary: Traditional visual grounding methods primarily focus on single-image
  scenarios with simple textual references. However, extending these methods to
  real-world scenarios that involve implicit and complex instructions,
  particularly in conjunction with multiple images, poses significant challenges,
  which is mainly due to the lack of advanced reasoning ability across diverse
  multi-modal contexts. In this work, we aim to address the more practical
  universal grounding task, and propose UniVG-R1, a reasoning guided multimodal
  large language model (MLLM) for universal visual grounding, which enhances
  reasoning capabilities through reinforcement learning (RL) combined with
  cold-start data. Specifically, we first construct a high-quality
  Chain-of-Thought (CoT) grounding dataset, annotated with detailed reasoning
  chains, to guide the model towards correct reasoning paths via supervised
  fine-tuning. Subsequently, we perform rule-based reinforcement learning to
  encourage the model to identify correct reasoning chains, thereby incentivizing
  its reasoning capabilities. In addition, we identify a difficulty bias arising
  from the prevalence of easy samples as RL training progresses, and we propose a
  difficulty-aware weight adjustment strategy to further strengthen the
  performance. Experimental results demonstrate the effectiveness of UniVG-R1,
  which achieves state-of-the-art performance on MIG-Bench with a 9.1%
  improvement over the previous method. Furthermore, our model exhibits strong
  generalizability, achieving an average improvement of 23.4% in zero-shot
  performance across four image and video reasoning grounding benchmarks. The
  project page can be accessed at https://amap-ml.github.io/UniVG-R1-page/.
paper_ai_summary: UniVG-R1, a reasoning-guided multimodal large language model, enhances visual grounding by leveraging reinforcement learning and a difficulty-aware strategy, achieving state-of-the-art results and strong generalizability.
paper_ai_keywords: multimodal large language model reasoning guided reinforcement learning cold-start data Chain-of-Thought dataset supervised fine-tuning rule-based reinforcement learning difficulty bias difficulty-aware weight adjustment