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: What is the effect of scaling the hybrid batch training strategy with larger multilingual models (e.g., mBERT vs. XLM-R) on zero-shot cross-lingual retrieval performance, as measured by MRR scores on MLQA and XQuAD?Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.0/10.