Omni-R1: Reinforcement Learning for Omnimodal Reasoning via Two-System Collaboration
Hao Zhong, Muzhi Zhu, Zongze Du, Zheng Huang, Canyu Zhao, Mingyu Liu, Wen Wang, Hao Chen, Chunhua Shen
Enabling intelligent systems to simultaneously process and reason over information from multiple modalities—such as text, video, and audio—in complex real-world scenarios, while performing planning and precise decision-making, has long been regarded as the ultimate goal in the field of artificial intelligence.Recently, with the rapid advancement of multimodal pretraining and supervised fine-tuning (SFT) techniques, a series of Omni-modal models have emerged, bringing us closer to this vision.However, current Omni-modal models still exhibit significant shortcomings in understanding and reasoning over long video and audio sequences, as well as in fine-grained, pixel-level comprehension tasks.On the other hand, while reinforcement learning (RL) has achieved remarkable success in enhancing the reasoning capabilities of MLLMs in pure-text domains, its application within Omni-modal models remains at an early stage.This is due to two major challenges: first, the lack of effective reasoning datasets and task formulations for training Omni-modal models; second, the effectiveness of existing RL techniques in Omni-modal settings has not yet been thoroughly validated.To address these issues, we focus on two of the most challenging tasks: Referring Audio-Visual Segmentation (RefAVS) and Referring Video Object Segmentation (REVOS).We decouple the complex video understanding task into two key subtasks: (1) identifying long-range keyframes in videos, and (2) generating task-specific re-captions.We propose a fully RL-based framework that leverages large-scale existing datasets to jointly optimize both capabilities of Omni-modal models.Experimental results demonstrate that our model achieves state-of-the-art performance on both the RefAVS and REVOS benchmarks.Furthermore, we show that our RL approach brings significant improvements on other general-purpose understanding tasks as well.