MM-RLHF: The Next Step Forward in Multimodal LLM Alignment
Despite notable advancements in Multimodal Large Language Models (MLLMs),
most state-of-the-art models have not undergone thorough alignment with human
preferences. This gap exists because current alignment research has primarily
achieved progress in specific areas (e.g., hallucination reduction), while the
broader question of whether aligning models with human preferences can
systematically enhance MLLM capability remains largely unexplored. To this end,
we introduce MM-RLHF, a dataset containing 120k fine-grained,
human-annotated preference comparison pairs. This dataset represents a
substantial advancement over existing resources, offering superior size,
diversity, annotation granularity, and quality. Leveraging this dataset, we
propose several key innovations to improve both the quality of reward models
and the efficiency of alignment algorithms. Notably, we introduce a
Critique-Based Reward Model, which generates critiques of model outputs before
assigning scores, offering enhanced interpretability and more informative
feedback compared to traditional scalar reward mechanisms. Additionally, we
propose Dynamic Reward Scaling, a method that adjusts the loss weight of each
sample according to the reward signal, thereby optimizing the use of
high-quality comparison pairs. Our approach is rigorously evaluated across
10 distinct dimensions and 27 benchmarks, with results
demonstrating significant and consistent improvements in model performance.
Specifically, fine-tuning LLaVA-ov-7B with MM-RLHF and our alignment algorithm
leads to a 19.5% increase in conversational abilities and a
60% improvement in safety.
  We have open-sourced the preference dataset, reward model, training and
evaluation code, as well as reward modeling and safety benchmarks. For more
details, please visit our project page: https://mm-rlhf.github.io.
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Yi-Fan Zhang
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Yi-Fan Zhang
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Tao Yu
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Haochen Tian
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Micheal Tian
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Peiyan Li
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Li Peiyan
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Jianshu Zeng
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Wulin Xie
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Yang Shi
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Yang Shi
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Huanyu Zhang
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Junkang Wu
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Junkang Wu
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Xue Wang
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Yibo Hu
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Bin Wen
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Fan Yang
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Zhang Zhang
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Tingting Gao
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Di Zhang
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Liang Wang
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Rong Jin
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Tieniu Tan
MM-RLHF: The Next Step Forward in Multimodal LLM Alignment
Despite notable advancements in Multimodal Large Language Models (MLLMs),
most state-of-the-art models have not undergone thorough alignment with human
preferences. This gap exists because current alignment research has primarily
achieved progress in specific areas (e.g., hallucination reduction), while the
broader question of whether aligning models with human preferences can
systematically enhance MLLM capability remains largely unexplored. To this end,
we introduce MM-RLHF, a dataset containing 120k fine-grained,
human-annotated preference comparison pairs. This dataset represents a
substantial advancement over existing resources, offering superior size,
diversity, annotation granularity, and quality. Leveraging this dataset, we
propose several key innovations to improve both the quality of reward models
and the efficiency of alignment algorithms. Notably, we introduce a
Critique-Based Reward Model, which generates critiques of model outputs before
assigning scores, offering enhanced interpretability and more informative
feedback compared to traditional scalar reward mechanisms. Additionally, we
propose Dynamic Reward Scaling, a method that adjusts the loss weight of each
sample according to the reward signal, thereby optimizing the use of
high-quality comparison pairs. Our approach is rigorously evaluated across
10 distinct dimensions and 27 benchmarks, with results
demonstrating significant and consistent improvements in model performance.
Specifically, fine-tuning LLaVA-ov-7B with MM-RLHF and our alignment algorithm
leads to a 19.5% increase in conversational abilities and a
60% improvement in safety.
  We have open-sourced the preference dataset, reward model, training and
evaluation code, as well as reward modeling and safety benchmarks. For more
details, please visit our project page: https://mm-rlhf.github.io.
MM-RLHF improves Multimodal Large Language Models' alignment with human preferences through a large preference dataset and innovative reward model techniques.
Multimodal Large Language Models MM-RLHF preference comparison pairs reward models Critique-Based Reward Model Dynamic Reward Scaling conversational abilities safety