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 hybrid batch strategy compare to independent training of monolingual, cross-lingual, and multilingual models in terms of zero-shot transferability to unseen languages on XQuAD and other multilingual benchmarks like XCOPA or PAWS-X?Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.8/10.