TinyLLaVA-Video-R1: Towards Smaller LMMs for Video Reasoning
Authors: name: Xingjian Zhang, name: Siwei Wen, name: Wenjun Wu, name: Lei Huang

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.