FairReason: Balancing Reasoning and Social Bias in MLLMs

• URL: http://arxiv.org/abs/2507.23067v2
• Date: Sat, 06 Sep 2025 03:03:04 GMT
• Title: FairReason: Balancing Reasoning and Social Bias in MLLMs
• Authors: Zhenyu Pan, Yutong Zhang, Jianshu Zhang, Haoran Lu, Haozheng Luo, Yuwei Han, Philip S. Yu, Manling Li, Han Liu,
• Abstract summary: Multimodal Large Language Models (MLLMs) already achieve state-of-the-art results across a wide range of tasks and modalities.<n>Recent studies explore advanced prompting schemes and post-training fine-tuning to push their reasoning ability further.
• Score: 54.26091556079722
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal Large Language Models (MLLMs) already achieve state-of-the-art results across a wide range of tasks and modalities. To push their reasoning ability further, recent studies explore advanced prompting schemes and post-training fine-tuning. Although these techniques improve logical accuracy, they frequently leave the models' outputs burdened with pronounced social biases. Clarifying how reasoning gains interact with bias mitigation-and whether the two objectives inherently trade off-therefore remains an open and pressing research problem. Our study begins by benchmarking three bias-mitigation strategies-supervised fine-uning (SFT), knowledge distillation (KD), and rule-based reinforcement learning (RL)-under identical conditions, establishing their baseline strengths and weaknesses. Building on these results, we vary the proportion of debias-focused and reasoning-centric samples within each paradigm to chart the reasoning-versus-bias trade-off. Our sweeps reveal a consistent sweet spot: a roughly 1:4 mix trained with reinforcement learning cuts stereotype scores by 10% while retaining 88% of the model's original reasoning accuracy, offering concrete guidance for balancing fairness and capability in MLLMs.

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