R1-Omni：基于强化学习的可解释全模态情感识别

R1-Omni: Explainable Omni-Multimodal Emotion Recognition with Reinforcing Learning

March 7, 2025

作者: Jiaxing Zhao, Xihan Wei, Liefeng Bo

cs.AI

摘要

在本研究中，我们首次将可验证奖励强化学习（RLVR）应用于全模态大语言模型，聚焦于情感识别这一视觉与听觉模态均起关键作用的任务。通过RLVR优化全模态模型，我们显著提升了其在三个核心方面的性能：推理能力、情感识别准确率以及泛化能力。RLVR的引入不仅提高了模型在分布内数据上的整体表现，还在分布外数据集评估中展现出卓越的鲁棒性。更重要的是，增强后的推理能力使得我们能够清晰分析不同模态，特别是视觉与听觉信息，在情感识别过程中的贡献度。这为多模态大语言模型的优化提供了宝贵的洞见。

English

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.