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 scaling of model size (e.g., small, base, large) interact with the hybrid batch training strategy in terms of zero-shot cross-lingual retrieval performance on the TyDi QA benchmark?Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.6/10.