Omni-Reward: Towards Generalist Omni-Modal Reward Modeling with
  Free-Form Preferences
Authors: name: Zhuoran Jin; name: Hongbang Yuan; name: Kejian Zhu; name: Jiachun Li; name: Pengfei Cao; name: Yubo Chen; name: Kang Liu; name: Jun Zhao
Published: 2025-10-27 15:53:20

Abstract: Reward models (RMs) play a critical role in aligning AI behaviors with human
preferences, yet they face two fundamental challenges: (1) Modality Imbalance,
where most RMs are mainly focused on text and image modalities, offering
limited support for video, audio, and other modalities; and (2) Preference
Rigidity, where training on fixed binary preference pairs fails to capture the
complexity and diversity of personalized preferences. To address the above
challenges, we propose Omni-Reward, a step toward generalist omni-modal reward
modeling with support for free-form preferences, consisting of: (1) Evaluation:
We introduce Omni-RewardBench, the first omni-modal RM benchmark with free-form
preferences, covering nine tasks across five modalities including text, image,
video, audio, and 3D; (2) Data: We construct Omni-RewardData, a multimodal
preference dataset comprising 248K general preference pairs and 69K
instruction-tuning pairs for training generalist omni-modal RMs; (3) Model: We
propose Omni-RewardModel, which includes both discriminative and generative
RMs, and achieves strong performance on Omni-RewardBench as well as other
widely used reward modeling benchmarks.