Active-O3: Empowering Multimodal Large Language Models with Active
  Perception via GRPO
Authors: name: Muzhi Zhu, name: Hao Zhong, name: Canyu Zhao, name: Zongze Du, name: Zheng Huang, name: Mingyu Liu, name: Hao Chen, name: Cheng Zou, name: Jingdong Chen, name: Ming Yang, name: Chunhua Shen

Active vision, also known as active perception, refers to the process of
actively selecting where and how to look in order to gather task-relevant
information. It is a critical component of efficient perception and
decision-making in humans and advanced embodied agents. Recently, the use of
Multimodal Large Language Models (MLLMs) as central planning and
decision-making modules in robotic systems has gained extensive attention.
However, despite the importance of active perception in embodied intelligence,
there is little to no exploration of how MLLMs can be equipped with or learn
active perception capabilities. In this paper, we first provide a systematic
definition of MLLM-based active perception tasks. We point out that the
recently proposed GPT-o3 model's zoom-in search strategy can be regarded as a
special case of active perception; however, it still suffers from low search
efficiency and inaccurate region selection. To address these issues, we propose
ACTIVE-O3, a purely reinforcement learning based training framework built on
top of GRPO, designed to equip MLLMs with active perception capabilities. We
further establish a comprehensive benchmark suite to evaluate ACTIVE-O3 across
both general open-world tasks, such as small-object and dense object grounding,
and domain-specific scenarios, including small object detection in remote
sensing and autonomous driving, as well as fine-grained interactive
segmentation. In addition, ACTIVE-O3 also demonstrates strong zero-shot
reasoning abilities on the V* Benchmark, without relying on any explicit
reasoning data. We hope that our work can provide a simple codebase and
evaluation protocol to facilitate future research on active perception in
MLLMs.