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 varying the ratio of monolingual, cross-lingual, and multilingual data in the hybrid batch training strategy on the XOR benchmark, as evaluated by mean reciprocal rank (MRR) and NDCG@10?Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.3/10.