arXiv Machine Learning October 12, 2026 By Zilong Huang, Kong Aik Lee, Zezhong Jin, Man-Wai Mak, Tatsuya Kawahara

EmoGavel: Grounding-Aware Reinforcement Learning with Verifiable Rewards for Multimodal Emotion Preference Learning

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EmoGavel is a reinforcement learning framework that incorporates verifiable rewards to improve multimodal emotion preference learning. It introduces grounded supervised fine‑tuning (Grounded SFT) to jointly learn preference prediction, error localization, repair, and missing‑evidence recovery, and then applies grounding‑aware group relative policy optimization (GA‑GRPO) to prioritize preference correctness and recover learning signals from all‑wrong groups. Experiments on EmoPrefer and controlled‑error evaluation sets demonstrate that EmoGavel enhances natural preference prediction, controlled‑error discrimination, and maintains strong error‑grounding performance.

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