Information retrieval across different languages is an increasingly important challenge in natural language processing. Recent approaches based on multilingual pre-trained language models have achieved remarkable success, yet they often optimize for either monolingual, cross-lingual, or multilingual retrieval performance at the expense of others. This paper proposes a novel hybrid batch training strategy to simultaneously improve zero-shot retrieval performance across monolingual, cross-lingual, and multilingual settings while mitigating language bias. The approach fine-tunes multilingual langResearch goal: How does the monolingual-to-multilingual data ratio in hybrid batch training affect the trade-off between zero-shot cross-lingual retrieval accuracy and monolingual retrieval accuracy on the BEIR benchmark?Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.7/10.