Self-Contradiction as Self-Improvement: Mitigating the Generation-Understanding Gap in MLLMs

• URL: http://arxiv.org/abs/2507.16663v1
• Date: Tue, 22 Jul 2025 14:56:39 GMT
• Title: Self-Contradiction as Self-Improvement: Mitigating the Generation-Understanding Gap in MLLMs
• Authors: Yujin Han, Hao Chen, Andi Han, Zhiheng Wang, Xinyu Lin, Yingya Zhang, Shiwei Zhang, Difan Zou,
• Abstract summary: We show that MLLMs exhibit self-contradiction where generation produces images deemed misaligned with input prompts based on the model's own understanding.<n>Our empirical results reveal that the self-contradiction mainly arises from weak generation that fails to align with prompts, rather than misunderstanding.<n>Applying standard post-training methods with such internal supervision successfully improves both generation and unification.
• Score: 29.790632461930947
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite efforts to unify multimodal generation and understanding tasks in a single model, we show these MLLMs exhibit self-contradiction where generation produces images deemed misaligned with input prompts based on the model's own understanding. We define a Nonunified score that quantifies such self-contradiction. Our empirical results reveal that the self-contradiction mainly arises from weak generation that fails to align with prompts, rather than misunderstanding. This capability asymmetry indicates the potential of leveraging self-contradiction for self-improvement, where the stronger model understanding guides the weaker generation to mitigate the generation-understanding gap. Applying standard post-training methods (e.g., SFT, DPO) with such internal supervision successfully improves both generation and unification. We discover a co-improvement effect on both generation and understanding when only fine-tuning the generation branch, a phenomenon known in pre-training but underexplored in post-training. Our analysis shows improvements stem from better detection of false positives that are previously incorrectly identified as prompt-aligned. Theoretically, we show the aligned training dynamics between generation and understanding allow reduced prompt-misaligned generations to also improve mismatch detection in the understanding branch. Additionally, the framework reveals a potential risk of co-degradation under poor supervision-an overlooked phenomenon that is empirically validated in our experiments. Notably, we find intrinsic metrics like Nonunified score cannot distinguish co-degradation from co-improvement, which highlights the necessity of data quality check. Finally, we propose a curriculum-based strategy based on our findings that gradually introduces harder samples as the model improves, leading to better unification and improved MLLM generation and understanding.

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