title: Improved Visual-Spatial Reasoning via R1-Zero-Like Training
summary: Increasing attention has been placed on improving the reasoning capacities of
  multi-modal large language models (MLLMs). As the cornerstone for AI agents
  that function in the physical realm, video-based visual-spatial intelligence
  (VSI) emerges as one of the most pivotal reasoning capabilities of MLLMs. This
  work conducts a first, in-depth study on improving the visual-spatial reasoning
  of MLLMs via R1-Zero-like training. Technically, we first identify that the
  visual-spatial reasoning capacities of small- to medium-sized Qwen2-VL models
  cannot be activated via Chain of Thought (CoT) prompts. We then incorporate
  GRPO training for improved visual-spatial reasoning, using the carefully
  curated VSI-100k dataset, following DeepSeek-R1-Zero. During the investigation,
  we identify the necessity to keep the KL penalty (even with a small value) in
  GRPO. With just 120 GPU hours, our vsGRPO-2B model, fine-tuned from
  Qwen2-VL-2B, can outperform the base model by 12.1% and surpass GPT-4o.
  Moreover, our vsGRPO-7B model, fine-tuned from Qwen2-VL-7B, achieves
  performance comparable to that of the best open-source model
  LLaVA-NeXT-Video-72B. Additionally, we compare vsGRPO to supervised fine-tuning
  and direct preference optimization baselines and observe strong performance
  superiority. The code and dataset will be available soon.
paper_authors._id: 67edf28e042e8ba3e95d1960
paper_authors.name: Zhenyi Liao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-03T13:44:50.157Z
paper_authors._id: 67ed94169723a841a7128c6f
paper_authors.avatarUrl: /avatars/950397084cc48c0885cf85bd3e5260b1.svg
paper_authors.fullname: ZhenYi Liao
paper_authors.type: user
paper_authors.user: ZhenYiao
paper_authors._id: 67edf28e042e8ba3e95d1961
paper_authors.name: Qingsong Xie
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-03T13:45:08.446Z
paper_authors._id: 64184911db24526c7c9ca035
paper_authors.avatarUrl: /avatars/2be15485bdd9e00857d4bff107d1577e.svg
paper_authors.fullname: qingsong xie
paper_authors.type: user
paper_authors.user: kongming11
paper_authors._id: 67edf28e042e8ba3e95d1962
paper_authors.name: Yanhao Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-03T13:45:23.767Z
paper_authors._id: 6527849a5fc08c60dc6a3120
paper_authors.avatarUrl: /avatars/54651dc48acd119c012520a616285e7a.svg
paper_authors.fullname: Yanhao Zhang
paper_authors.type: user
paper_authors.user: YanhaoZhang
paper_authors._id: 67edf28e042e8ba3e95d1963
paper_authors.name: Zijian Kong
paper_authors._id: 67edf28e042e8ba3e95d1964
paper_authors.name: Haonan Lu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-03T13:45:39.183Z
paper_authors._id: 603864181b642aef9350af81
paper_authors.avatarUrl: /avatars/27ec0b885cbedef007876c8c98c69d15.svg
paper_authors.fullname: Haonan Lu
paper_authors.type: user
paper_authors.user: luhaonan20
paper_authors._id: 67edf28e042e8ba3e95d1965
paper_authors.name: Zhenyu Yang
paper_authors._id: 67edf28e042e8ba3e95d1966
paper_authors.name: Zhijie Deng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-03T07:52:14.204Z
paper_authors._id: 64bba541da140e461924dfed
paper_authors.avatarUrl: /avatars/367993765b0ca3734b2b100db33ed787.svg
paper_authors.fullname: zhijie deng
paper_authors.type: user
paper_authors.user: zhijie3
paper_title: Improved Visual-Spatial Reasoning via R1-Zero-Like Training
paper_summary: Increasing attention has been placed on improving the reasoning capacities of
  multi-modal large language models (MLLMs). As the cornerstone for AI agents
  that function in the physical realm, video-based visual-spatial intelligence
  (VSI) emerges as one of the most pivotal reasoning capabilities of MLLMs. This
  work conducts a first, in-depth study on improving the visual-spatial reasoning
  of MLLMs via R1-Zero-like training. Technically, we first identify that the
  visual-spatial reasoning capacities of small- to medium-sized Qwen2-VL models
  cannot be activated via Chain of Thought (CoT) prompts. We then incorporate
  GRPO training for improved visual-spatial reasoning, using the carefully
  curated VSI-100k dataset, following DeepSeek-R1-Zero. During the investigation,
  we identify the necessity to keep the KL penalty (even with a small value) in
  GRPO. With just 120 GPU hours, our vsGRPO-2B model, fine-tuned from
  Qwen2-VL-2B, can outperform the base model by 12.1% and surpass GPT-4o.
  Moreover, our vsGRPO-7B model, fine-tuned from Qwen2-VL-7B, achieves
  performance comparable to that of the best open-source model
  LLaVA-NeXT-Video-72B. Additionally, we compare vsGRPO to supervised fine-tuning
  and direct preference optimization baselines and observe strong performance
  superiority. The code and dataset will be available soon.
paper_ai_summary: The study enhances visual-spatial reasoning in multi-modal large language models through GRPO training using the VSI-100k dataset, demonstrating significant performance improvements over base models.
paper_ai_keywords: multi-modal large language models visual-spatial intelligence R1-Zero-like training Chain of Thought GRPO KL penalty VSI-100k vsGRPO Qwen2-VL GPT-4 LLaVA-NeXT-Video supervised fine-tuning direct preference optimization