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 varying the ratio of monolingual to cross-lingual data in hybrid batch training affect zero-shot retrieval performance on XOR and MLQA benchmarks when measured by mRR and NDCG?Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10.