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: Does the synergistic hybrid batch training strategy improve zero-shot cross-lingual retrieval accuracy for languages with varying typological distances (e.g., Indo-European vs. Uralic) in the XTREME-R benchmark when compared to standard multilingual fine-tuning?Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.6/10.