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UniRM: Multi-Head Scalar Reward Model for Multimodal Moderation

UniRM is a multi-head scalar reward model that provides interpretable, attribute-level scoring for multimodal moderation.
It is designed to support policy optimization for open-ended reasoning in UniMod, especially for the posterior response stage where deterministic labels are absent.

UniRM decouples reward attribution into multiple dimensions so the model can distinguish stylistic quality from safety boundaries (privacy, bias, toxicity, legality), enabling transparent diagnosis and stable optimization.

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Demo Video

UniRM demo video:

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Quick Start (Gradio)

Below is a minimal Gradio demo that loads UniRM and returns multi-head scores for a (prompt, response, optional image) triple.

git clone https://github.com/TideDra/lmm-r1.git
cd lmm-r1
pip install -e .[vllm]
pip install flash_attn --no-build-isolation
python unirm.py --model_path {PATH_TO_UNIRM}