Euphemisms are culturally variable and often ambiguous, posing challenges for language models, especially in low-resource settings. This paper investigates how cross-lingual transfer via sequential fine-tuning affects euphemism detection across five languages: English, Spanish, Chinese, Turkish, and Yoruba. We compare sequential fine-tuning with monolingual and simultaneous fine-tuning using XLM-R and mBERT, analyzing how performance is shaped by language pairings, typological features, and pretraining coverage. Results show that sequential fine-tuning with a high-resource L1 improves L2 perfoResearch goal: What is the comparative effect of English intermediate-task fine-tuning versus direct target-language fine-tuning on alignment metrics for under-represented languages in cross-lingual transfer scenarios?Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.8/10.