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 impact of the hybrid batch strategy on multilingual zero-shot retrieval performance when scaling the number of languages in the training batch, measured by mean reciprocal rank (MRR) on the XTD benchmark?Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.7/10.