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 performance of the hybrid batch training strategy compare to state-of-the-art multilingual language models like XLM-R or mT5 on the MIRACL benchmark when evaluated with zero-shot retrieval accuracy for both high-resource and low-resource language pairs?Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.3/10.