EMO-R3: Reflective Reinforcement Learning for Emotional Reasoning in Multimodal Large Language Models

2026-02-27

AI Summary

Existing multimodal large language models struggle with the complexity and subjectivity of human emotions, exhibiting limited generalization, poor interpretability, and misalignment between reinforcement learning strategies and affective cognition. To address these issues, this work proposes a structured emotional reasoning mechanism that guides the model through step-by-step affective inference. Furthermore, it introduces a reflective emotional reward mechanism that re-evaluates the reasoning process based on image-text consistency and emotional coherence. This approach significantly enhances performance across multiple visual emotion understanding benchmarks while improving the interpretability and self-reflective capacity of emotional reasoning, thereby better aligning with human affective cognitive processes.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Multimodal Large Language Models (MLLMs) have shown remarkable progress in visual reasoning and understanding tasks but still struggle to capture the complexity and subjectivity of human emotions. Existing approaches based on supervised fine-tuning often suffer from limited generalization and poor interpretability, while reinforcement learning methods such as Group Relative Policy Optimization fail to align with the intrinsic characteristics of emotional cognition. To address these challenges, we propose Reflective Reinforcement Learning for Emotional Reasoning (EMO-R3), a framework designed to enhance the emotional reasoning ability of MLLMs. Specifically, we introduce Structured Emotional Thinking to guide the model to perform step-by-step emotional reasoning in a structured and interpretable manner, and design a Reflective Emotional Reward that enables the model to re-evaluate its reasoning based on visual-text consistency and emotional coherence. Extensive experiments demonstrate that EMO-R3 significantly improves both the interpretability and emotional intelligence of MLLMs, achieving superior performance across multiple visual emotional understanding benchmarks.

Problem

• Multimodal Large Language Models
• Emotional Reasoning
• Emotion Understanding
• Visual Emotion

Innovation

• Reflective Reinforcement Learning
• Emotional Reasoning
• Multimodal Large Language Models
• Structured Emotional Thinking
• Reflective Emotional Reward