title: R1-Omni: Explainable Omni-Multimodal Emotion Recognition with
    Reinforcing Learning
summary: In this work, we present the first application of Reinforcement Learning with
  Verifiable Reward (RLVR) to an Omni-multimodal large language model in the
  context of emotion recognition, a task where both visual and audio modalities
  play crucial roles. We leverage RLVR to optimize the Omni model, significantly
  enhancing its performance in three key aspects: reasoning capability, emotion
  recognition accuracy, and generalization ability. The introduction of RLVR not
  only improves the model's overall performance on in-distribution data but also
  demonstrates superior robustness when evaluated on out-of-distribution
  datasets. More importantly, the improved reasoning capability enables clear
  analysis of the contributions of different modalities, particularly visual and
  audio information, in the emotion recognition process. This provides valuable
  insights into the optimization of multimodal large language models.
paper_authors._id: 67ce5f2389663abdbc364495
paper_authors.name: Jiaxing Zhao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-10T10:12:14.110Z
paper_authors._id: 6686044047f2a33570e59e31
paper_authors.avatarUrl: /avatars/2656bf2cecd6d7cbffd0a912a54d25de.svg
paper_authors.fullname: Jiaxing Zhao
paper_authors.type: user
paper_authors.user: StarJiaxing
paper_authors._id: 67ce5f2389663abdbc364496
paper_authors.name: Xihan Wei
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-12T10:02:49.458Z
paper_authors._id: 67cfb923dfdb000d159407da
paper_authors.avatarUrl: /avatars/6cc1da8f83ab7519cf240f7cb02b1b1c.svg
paper_authors.fullname: wei
paper_authors.type: user
paper_authors.user: xihanwei
paper_authors._id: 67ce5f2389663abdbc364497
paper_authors.name: Liefeng Bo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-10T10:12:31.341Z
paper_authors._id: 63d0cc736b985b0f25d0412c
paper_authors.avatarUrl: /avatars/3eb8c79f9a7c4c819038ea7b04e323dd.svg
paper_authors.fullname: Bo
paper_authors.type: user
paper_authors.user: Liefeng
paper_title: R1-Omni: Explainable Omni-Multimodal Emotion Recognition with
    Reinforcing Learning
paper_summary: In this work, we present the first application of Reinforcement Learning with
  Verifiable Reward (RLVR) to an Omni-multimodal large language model in the
  context of emotion recognition, a task where both visual and audio modalities
  play crucial roles. We leverage RLVR to optimize the Omni model, significantly
  enhancing its performance in three key aspects: reasoning capability, emotion
  recognition accuracy, and generalization ability. The introduction of RLVR not
  only improves the model's overall performance on in-distribution data but also
  demonstrates superior robustness when evaluated on out-of-distribution
  datasets. More importantly, the improved reasoning capability enables clear
  analysis of the contributions of different modalities, particularly visual and
  audio information, in the emotion recognition process. This provides valuable
  insights into the optimization of multimodal large language models.
paper_ai_keywords: Reinforcement Learning with Verifiable Reward (RLVR) Omni-multimodal large language model emotion recognition visual and audio modalities reasoning capability emotion recognition accuracy generalization ability out-of-distribution datasets multimodal large language models