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 the paper affect zero-shot cross-lingual retrieval performance on underrepresented language families compared to separate monolingual and multilingual training objectives, as measured by MRR and nDCG scores on the MIRACL benchmark?Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.5/10.