Pluralistic value alignment asks whether a language model can adapt its judgements to distinct, locally grounded value systems rather than reflecting a single aggregated notion of human preference. The PlurVA-LLM shared task evaluates this setting by unifying three independently constructed, culturally grounded multiple-choice value dilemmas across Chinese, Indonesian, and Sri Lankan datasets. We present a single-model submission based on Qwen3.5-4B with LoRA fine-tuning, trained only on the 1,359 labelled development instances provided by the task. Our method combines three ideas motivated directly by the task structure: soft-label supervision for Indonesian annotator-vote distributions, consistency regularisation across option permutations to reduce answer-position bias, and a staged multilingual curriculum with cross-validation-based iteration selection. On the hidden test set, our system achieves a macro-average accuracy of 0.8062, with 0.7788 on Chinese, 0.7139 on Indonesian, and 0.9260 on Sri Lankan, outperforming a manually tuned reference curriculum from our internal development. These results show that a single parameter-efficient model can unify culturally and structurally distinct value-alignment benchmarks under a low-resource training budget.