arXiv:2602.00846v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) struggle with alignment due to the limitations of existing reward models (RMs), which are predominantly vision-centric, dependent on costly human labels, and provide opaque scalar scores that fail to capture nuanced reasoning, leading to brittle alignment. We present Omni-RRM, an \textbf{Omni}-modal \textbf{R}ubric-grounded \textbf{R}eward \textbf{M}odel that generates multi-dimensional reward signals across text, image, video, and audio. To overcome the high cost and inherent inconsistency of human-ce
The rapid advancement of MLLMs and the increasing economic investment in AI agents necessitate more sophisticated and cost-effective reward modeling, pushing researchers to innovate beyond current limitations.
Improved reward models are critical for aligning advanced AI systems, particularly MLLMs and AI agents, making them more robust, reliable, and capable of nuanced reasoning, which directly impacts their commercial viability and safety.
Reward modeling moves from being predominantly vision-centric and human-label dependent to omni-modal and automatically generated, enabling multi-dimensional feedback for more complex AI systems.
- · AI platform developers
- · Multimodal LLM companies
- · AI safety researchers
- · Companies adopting AI agents
- · Providers of rudimentary human-labeled AI datasets
- · AI systems with poor alignment
- · Companies relying on brittle scalar reward models
AI models, especially MLLMs, will become significantly more aligned with human intent and complex task objectives.
The development and deployment of truly autonomous AI agents will accelerate due to more effective and scalable alignment mechanisms.
This could contribute to a paradigm shift in how AI systems learn and are controlled, potentially reducing the need for constant human oversight in some domains.
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Read at arXiv cs.CL