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: Can hybrid batch training (mixing monolingual and cross-lingual data) during intermediate fine-tuning enhance downstream performance on PAWS-X compared to monolingual batch training?Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.5/10.