title: InfiR : Crafting Effective Small Language Models and Multimodal Small
    Language Models in Reasoning
summary: Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs)
  have made significant advancements in reasoning capabilities. However, they
  still face challenges such as high computational demands and privacy concerns.
  This paper focuses on developing efficient Small Language Models (SLMs) and
  Multimodal Small Language Models (MSLMs) that retain competitive reasoning
  abilities. We introduce a novel training pipeline that enhances reasoning
  capabilities and facilitates deployment on edge devices, achieving
  state-of-the-art performance while minimizing development costs. \InfR~ aims to
  advance AI systems by improving reasoning, reducing adoption barriers, and
  addressing privacy concerns through smaller model sizes. Resources are
  available at https://github. com/Reallm-Labs/InfiR.
paper_authors._id: 67b6f629d9da6999328e38f5
paper_authors.name: Congkai Xie
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-20T17:12:49.025Z
paper_authors._id: 6719f1ad725123d503b5ef3c
paper_authors.avatarUrl: /avatars/08e1be1f4afa1b6e1501a15cdb786a47.svg
paper_authors.fullname: Congkai Xie
paper_authors.type: user
paper_authors.user: congkai
paper_authors._id: 67b6f629d9da6999328e38f6
paper_authors.name: Shuo Cai
paper_authors._id: 67b6f629d9da6999328e38f7
paper_authors.name: Wenjun Wang
paper_authors._id: 67b6f629d9da6999328e38f8
paper_authors.name: Pengxiang Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-20T17:13:23.938Z
paper_authors._id: 65d6bd8ebb862d1b367580fe
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/65d6bd8ebb862d1b367580fe/Pf5o-Oxi2IZxJPa-ZgEtV.jpeg
paper_authors.fullname: Pengxiang Li
paper_authors.type: user
paper_authors.user: PengxiangLi
paper_authors._id: 67b6f629d9da6999328e38f9
paper_authors.name: Zhijie Sang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-20T17:13:31.244Z
paper_authors._id: 6719f2569665701fd6a6a43d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/8gS7Tzr6rIZMl-8sS1x_r.png
paper_authors.fullname: Zhijie Sang
paper_authors.type: user
paper_authors.user: SANGZHIJIE
paper_authors._id: 67b6f629d9da6999328e38fa
paper_authors.name: Kejing Yang
paper_authors._id: 67b6f629d9da6999328e38fb
paper_authors.name: Yiming Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-20T17:13:47.213Z
paper_authors._id: 626309292597e1cb7c76ab6f
paper_authors.avatarUrl: /avatars/594b9ccfc5f3aa61613b4bc3158fed4f.svg
paper_authors.fullname: Yiming Zhang
paper_authors.type: user
paper_authors.user: yimingzhang
paper_authors._id: 67b6f629d9da6999328e38fc
paper_authors.name: Zhen Li
paper_authors._id: 67b6f629d9da6999328e38fd
paper_authors.name: Guanghao Zhu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-20T17:13:55.619Z
paper_authors._id: 670f8a7d4e3a710738fd13cc
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/2ITxPXZCAzdY-LhbNA6NI.png
paper_authors.fullname: Guanghao
paper_authors.type: user
paper_authors.user: GuanghaoZhu
paper_authors._id: 67b6f629d9da6999328e38fe
paper_authors.name: Zeyu Liu
paper_authors._id: 67b6f629d9da6999328e38ff
paper_authors.name: Yang Yu
paper_authors._id: 67b6f629d9da6999328e3900
paper_authors.name: Yuhang Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-22T10:16:24.806Z
paper_authors._id: 62722849517c0ca41f7cd13d
paper_authors.avatarUrl: /avatars/bb1f8f2f2665944930cb5a7ce19c47d4.svg
paper_authors.fullname: Yuhang Liu
paper_authors.type: user
paper_authors.user: SiriusL
paper_authors._id: 67b6f629d9da6999328e3901
paper_authors.name: Su Lu
paper_authors._id: 67b6f629d9da6999328e3902
paper_authors.name: Baoyi He
paper_authors._id: 67b6f629d9da6999328e3903
paper_authors.name: Qi Zhou
paper_authors._id: 67b6f629d9da6999328e3904
paper_authors.name: Xiaotian Han
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-20T17:32:33.779Z
paper_authors._id: 650dde4ce14eeb01d42b37a1
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/650dde4ce14eeb01d42b37a1/n5Yv24uofZ2XJjXdYCrKd.png
paper_authors.fullname: Xiaotian Han
paper_authors.type: user
paper_authors.user: xiaotianhan
paper_authors._id: 67b6f629d9da6999328e3905
paper_authors.name: Jianbo Yuan
paper_authors._id: 67b6f629d9da6999328e3906
paper_authors.name: Shengyu Zhang
paper_authors._id: 67b6f629d9da6999328e3907
paper_authors.name: Fei Wu
paper_authors._id: 67b6f629d9da6999328e3908
paper_authors.name: Hongxia Yang
paper_title: InfiR : Crafting Effective Small Language Models and Multimodal Small
    Language Models in Reasoning
paper_summary: Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs)
  have made significant advancements in reasoning capabilities. However, they
  still face challenges such as high computational demands and privacy concerns.
  This paper focuses on developing efficient Small Language Models (SLMs) and
  Multimodal Small Language Models (MSLMs) that retain competitive reasoning
  abilities. We introduce a novel training pipeline that enhances reasoning
  capabilities and facilitates deployment on edge devices, achieving
  state-of-the-art performance while minimizing development costs. \InfR~ aims to
  advance AI systems by improving reasoning, reducing adoption barriers, and
  addressing privacy concerns through smaller model sizes. Resources are
  available at https://github. com/Reallm-Labs/InfiR.
paper_ai_summary: Researchers developed Small Language Models and Multimodal Small Language Models to enhance reasoning capabilities while reducing computational demands and privacy concerns.
paper_ai_keywords: Large Language Models Multimodal Large Language Models Small Language Models Multimodal Small Language Models training pipeline reasoning capabilities edge devices state-of-the-art performance privacy concerns InfR