title: TinyLLaVA-Video-R1: Towards Smaller LMMs for Video Reasoning
summary: Recently, improving the reasoning ability of large multimodal models (LMMs)
  through reinforcement learning has made great progress. However, most existing
  works are based on highly reasoning-intensive datasets such as mathematics and
  code, and researchers generally choose large-scale models as the foundation. We
  argue that exploring small-scale models' reasoning capabilities remains
  valuable for researchers with limited computational resources. Moreover,
  enabling models to explain their reasoning processes on general
  question-answering datasets is equally meaningful. Therefore, we present the
  small-scale video reasoning model TinyLLaVA-Video-R1. Based on TinyLLaVA-Video,
  a traceably trained video understanding model with no more than 4B parameters,
  it not only demonstrates significantly improved reasoning and thinking
  capabilities after using reinforcement learning on general Video-QA datasets,
  but also exhibits the emergent characteristic of "aha moments". Furthermore, we
  share a series of experimental findings, aiming to provide practical insights
  for future exploration of video reasoning (thinking) abilities in small-scale
  models. It is available at https://github.com/ZhangXJ199/TinyLLaVA-Video-R1.
paper_authors._id: 67fdceb2df4261c001394f59
paper_authors.name: Xingjian Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-15T07:54:14.629Z
paper_authors._id: 66448136bfe15e84d3987372
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/66448136bfe15e84d3987372/SFEbWGgqhdZjxCS7qF3YL.jpeg
paper_authors.fullname: Zhang Xingjian
paper_authors.type: user
paper_authors.user: Zhang199
paper_authors._id: 67fdceb2df4261c001394f5a
paper_authors.name: Siwei Wen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-15T08:29:04.694Z
paper_authors._id: 67f72edbc791c2e0f938203d
paper_authors.avatarUrl: /avatars/c8e6d5a2f2122482e5fab7c6438440b5.svg
paper_authors.fullname: si wei wen
paper_authors.type: user
paper_authors.user: wenzz1
paper_authors._id: 67fdceb2df4261c001394f5b
paper_authors.name: Wenjun Wu
paper_authors._id: 67fdceb2df4261c001394f5c
paper_authors.name: Lei Huang
paper_title: TinyLLaVA-Video-R1: Towards Smaller LMMs for Video Reasoning
paper_summary: Recently, improving the reasoning ability of large multimodal models (LMMs)
  through reinforcement learning has made great progress. However, most existing
  works are based on highly reasoning-intensive datasets such as mathematics and
  code, and researchers generally choose large-scale models as the foundation. We
  argue that exploring small-scale models' reasoning capabilities remains
  valuable for researchers with limited computational resources. Moreover,
  enabling models to explain their reasoning processes on general
  question-answering datasets is equally meaningful. Therefore, we present the
  small-scale video reasoning model TinyLLaVA-Video-R1. Based on TinyLLaVA-Video,
  a traceably trained video understanding model with no more than 4B parameters,
  it not only demonstrates significantly improved reasoning and thinking
  capabilities after using reinforcement learning on general Video-QA datasets,
  but also exhibits the emergent characteristic of "aha moments". Furthermore, we
  share a series of experimental findings, aiming to provide practical insights
  for future exploration of video reasoning (thinking) abilities in small-scale
  models. It is available at https://github.com/ZhangXJ199/TinyLLaVA-Video-R1.
paper_ai_summary: TinyLLaVA-Video-R1, a small-scale video reasoning model, demonstrates improved reasoning capabilities and exhibits "aha moments" after reinforcement learning on general Video-QA datasets.
paper_ai_keywords: large multimodal models reinforcement learning small-scale models general question-answering datasets video understanding model TinyLLaVA-Video Video-QA datasets reasoning and thinking capabilities aha moments