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 effect of varying the order of sequential fine-tuning tasks on cross-lingual euphemism detection performance across languages in XLM-R-Large, and how does it compare to simultaneous fine-tuning in terms of F1 score?Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10.