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 training strategy proposed in this paper compare to state-of-the-art multilingual models like mBART or XLM-R on the MIRACL benchmark for zero-shot cross-lingual retrieval in terms of precision and recall metrics?Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.9/10.