While vocabulary expansion scaling laws are well-established for high-resource languages, they remain unverified in low-resource settings. This gap is particularly critical for Byte-level BPE (BBPE), where constrained vocabulary sizes often fail to capture the rich morphemes of complex scripts, leading to severe over-segmentation in languages such as Mongolian, Tibetan, and Uyghur. We systematically investigate jointly-scaled trilingual vocabulary for these languages (140 to 195,000 tokens) across BPE (Llama 2) and BBPE (Qwen2.5/3) architectures. Our results reveal that BBPE follows a "decline-then-rise" pattern, requiring a 9,000-token threshold (3,000 per language) to trigger non-linear performance gains and inference acceleration, whereas BPE improves monotonically. Using Pareto Frontier Analysis, we identify an optimal 79,500-token configuration for BBPE that reduces continuous pre-training duration by over 71% across 1.5B to 8B parameter models while consistently enhancing downstream performance.