Audio Captioning (AC) plays a pivotal role in enhancing audio-text cross-modal understanding during the pretraining and finetuning of Multimodal LLMs (MLLMs). To strengthen this alignment, recent works propose Audio Difference Captioning (ADC), which takes multiple audio inputs and encourages the model to describe their differences, thereby promoting fine-grained discrimination. However, despite its effectiveness, ADC introduces a semantic gap between input audios-often rich in diverse events-and the brief, difference-focused short caption. This deviation from AC-style task causes a mismatch with the pretraining objective, leading to catastrophic forgetting. To address this, we propose Audio Commonality Captioning (ACC), a comparably challenging but gentler alternative that guides the model to capture shared semantics across audio clips rather than detailed differences. Experiments show that ACC not only improves audio-text understanding on captioning benchmarks but also better preserves general capabilities across diverse speech and music tasks, confirming its ability to enable more robust cross-modal understanding and achieve a better balance between generalization and task-specific performance in MLLMs.