Omni-R1: Towards the Unified Generative Paradigm for Multimodal Reasoning
Multimodal Large Language Models (MLLMs) are making significant progress in multimodal reasoning. Early approaches focus on pure text-based reasoning. More recent studies have incorporated multimodal information into the reasoning steps; however, they often follow a single task-specific reasoning pattern, which limits their generalizability across various multimodal tasks. In fact, there are numerous multimodal tasks requiring diverse reasoning skills, such as zooming in on a specific region or marking an object within an image. To address this, we propose unified generative multimodal reasoning, which unifies diverse multimodal reasoning skills by generating intermediate images during the reasoning process. We instantiate this paradigm with Omni-R1, a two-stage SFT+RL framework featuring perception alignment loss and perception reward, thereby enabling functional image generation. Additionally, we introduce Omni-R1-Zero, which eliminates the need for multimodal annotations by bootstrapping step-wise visualizations from text-only reasoning data. Empirical results show that Omni-R1 achieves unified generative reasoning across a wide range of multimodal tasks, and Omni-R1-Zero can match or even surpass Omni-R1 on average, suggesting a promising direction for generative multimodal reasoning.
6968787c0ac10a06522f6aca
Dongjie Cheng
6968787c0ac10a06522f6acb
Yongqi Li
6968787c0ac10a06522f6acc
Zhixin Ma
6968787c0ac10a06522f6acd
Hongru Cai
claimed_verified
2026-01-27T09:44:09.688Z
653a111eee5888edef9182cf
https://cdn-avatars.huggingface.co/v1/production/uploads/653a111eee5888edef9182cf/7jQ08JDk2UEBla91At8QR.jpeg
Hongru Cai
HongruCai
user
HongruCai
6968787c0ac10a06522f6ace
Yupeng Hu
6968787c0ac10a06522f6acf
Wenjie Wang
6968787c0ac10a06522f6ad0
Liqiang Nie
6968787c0ac10a06522f6ad1
Wenjie Li
Omni-R1: Towards the Unified Generative Paradigm for Multimodal Reasoning
Multimodal Large Language Models (MLLMs) are making significant progress in multimodal reasoning. Early approaches focus on pure text-based reasoning. More recent studies have incorporated multimodal information into the reasoning steps; however, they often follow a single task-specific reasoning pattern, which limits their generalizability across various multimodal tasks. In fact, there are numerous multimodal tasks requiring diverse reasoning skills, such as zooming in on a specific region or marking an object within an image. To address this, we propose unified generative multimodal reasoning, which unifies diverse multimodal reasoning skills by generating intermediate images during the reasoning process. We instantiate this paradigm with Omni-R1, a two-stage SFT+RL framework featuring perception alignment loss and perception reward, thereby enabling functional image generation. Additionally, we introduce Omni-R1-Zero, which eliminates the need for multimodal annotations by bootstrapping step-wise visualizations from text-only reasoning data. Empirical results show that Omni-R1 achieves unified generative reasoning across a wide range of multimodal tasks, and Omni-R1-Zero can match or even surpass Omni-R1 on average, suggesting a promising direction for generative multimodal reasoning.
Unified generative multimodal reasoning approach enables diverse reasoning skills through intermediate image generation, with a two-stage SFT+RL framework and a text-only bootstrapping variant.
Multimodal Large Language Models multimodal reasoning generative multimodal reasoning perception alignment loss perception reward SFT+RL framework intermediate image generation Omni-R1 Omni-R1-Zero