title: LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through
    Two-Stage Rule-Based RL
summary: Enhancing reasoning in Large Multimodal Models (LMMs) faces unique challenges
  from the complex interplay between visual perception and logical reasoning,
  particularly in compact 3B-parameter architectures where architectural
  constraints limit reasoning capacity and modality alignment.
    While rule-based reinforcement learning (RL) excels in text-only domains, its
  multimodal extension confronts two critical barriers: (1) data limitations due
  to ambiguous answers and scarce complex reasoning examples, and (2) degraded
  foundational reasoning induced by multimodal pretraining.
    To address these challenges, we propose \method, a two-stage
  framework adapting rule-based RL for multimodal reasoning through
  Foundational Reasoning Enhancement (FRE) followed by
  Multimodal Generalization Training (MGT). The FRE stage first
  strengthens reasoning abilities using text-only data with rule-based RL, then
  the MGT stage generalizes these reasoning capabilities to multimodal domains.
    Experiments on Qwen2.5-VL-Instruct-3B demonstrate that \method achieves
  4.83\% and 4.5\% average improvements over baselines in multimodal and
  text-only benchmarks, respectively, with a 3.63\% gain in complex Football Game
  tasks. These results validate that text-based reasoning enhancement enables
  effective multimodal generalization, offering a data-efficient paradigm that
  bypasses costly high-quality multimodal training data.
paper_authors._id: 67d04f248f79213c2fc0ba04
paper_authors.name: Yingzhe Peng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-12T15:27:26.416Z
paper_authors._id: 64e82a99ad1bac272b73b5ab
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64e82a99ad1bac272b73b5ab/AL2bPCy5rrGxG1YrzJ76Z.jpeg
paper_authors.fullname: YingzhePeng
paper_authors.type: user
paper_authors.user: ColeYzzzz
paper_authors._id: 67d04f248f79213c2fc0ba05
paper_authors.name: Gongrui Zhang
paper_authors._id: 67d04f248f79213c2fc0ba06
paper_authors.name: Miaosen Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-12T15:25:22.352Z
paper_authors._id: 62a30bf72dac39c2173c0a8c
paper_authors.avatarUrl: /avatars/15fb1ea3dcc7ccd8bc8002ce282e27b3.svg
paper_authors.fullname: Miaosen Zhang
paper_authors.type: user
paper_authors.user: Miaosen
paper_authors._id: 67d04f248f79213c2fc0ba07
paper_authors.name: Zhiyuan You
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-12T15:25:53.095Z
paper_authors._id: 64c8f16736c11430f31d9bf6
paper_authors.avatarUrl: /avatars/6907df1fedbcedc63046e7457a6b5950.svg
paper_authors.fullname: zhiyuanyou
paper_authors.type: user
paper_authors.user: zhiyuanyou
paper_authors._id: 67d04f248f79213c2fc0ba08
paper_authors.name: Jie Liu
paper_authors._id: 67d04f248f79213c2fc0ba09
paper_authors.name: Qipeng Zhu
paper_authors._id: 67d04f248f79213c2fc0ba0a
paper_authors.name: Kai Yang
paper_authors._id: 67d04f248f79213c2fc0ba0b
paper_authors.name: Xingzhong Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-12T15:26:41.658Z
paper_authors._id: 63216ccd4bf43bca61997ad3
paper_authors.avatarUrl: /avatars/45c769b94a39df7b688f50e54d2c6223.svg
paper_authors.fullname: Xingzhong Xu
paper_authors.type: user
paper_authors.user: Xingzhong
paper_authors._id: 67d04f248f79213c2fc0ba0c
paper_authors.name: Xin Geng
paper_authors._id: 67d04f248f79213c2fc0ba0d
paper_authors.name: Xu Yang
paper_title: LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through
    Two-Stage Rule-Based RL
paper_summary: Enhancing reasoning in Large Multimodal Models (LMMs) faces unique challenges
  from the complex interplay between visual perception and logical reasoning,
  particularly in compact 3B-parameter architectures where architectural
  constraints limit reasoning capacity and modality alignment.
    While rule-based reinforcement learning (RL) excels in text-only domains, its
  multimodal extension confronts two critical barriers: (1) data limitations due
  to ambiguous answers and scarce complex reasoning examples, and (2) degraded
  foundational reasoning induced by multimodal pretraining.
    To address these challenges, we propose \method, a two-stage
  framework adapting rule-based RL for multimodal reasoning through
  Foundational Reasoning Enhancement (FRE) followed by
  Multimodal Generalization Training (MGT). The FRE stage first
  strengthens reasoning abilities using text-only data with rule-based RL, then
  the MGT stage generalizes these reasoning capabilities to multimodal domains.
    Experiments on Qwen2.5-VL-Instruct-3B demonstrate that \method achieves
  4.83\% and 4.5\% average improvements over baselines in multimodal and
  text-only benchmarks, respectively, with a 3.63\% gain in complex Football Game
  tasks. These results validate that text-based reasoning enhancement enables
  effective multimodal generalization, offering a data-efficient paradigm that
  bypasses costly high-quality multimodal training data.
paper_ai_keywords: Large Multimodal Models (LMMs) visual perception logical reasoning 3B-parameter architectures rule-based reinforcement learning (RL) multimodal extension ambiguous answers complex reasoning examples degraded foundational reasoning multimodal pretraining Foundational Reasoning Enhancement (FRE) Multimodal Generalization Training (MGT) Qwen2.5-VL-Instruct-3B multimodal benchmarks text-only benchmarks complex Football Game tasks text-based reasoning enhancement data-efficient paradigm