Machine translation systems often preserve meaning but systematically flatten linguistic style, a failure that is most acute when translating into non-Western languages.
The RASTA framework of Havaldar et al. (2025a) addresses this through retrieval-augmented stylistic alignment, but relies on GPT-4, a closed, proprietary model.
We investigate whether an open-weight translation specialist, Hunyuan-MT-7B, can serve as an open-weight alternative.
We evaluate three variants of the model, the base model, a LoRA supervised fine-tune, and a GRPO-steered policy.
We do this across three style dimensions (formality, politeness, and intimacy) and twelve directed language pairs, measuring style alignment with the correlation-based metric $\mathcal{A}$.
We find that fine-tuning does not reliably improve alignment: the three variants are statistically indistinguishable in aggregate.
The open backbone does not match RASTA-GPT-4 overall, but the shortfall is concentrated in formality and largely closes for intimacy and Chinese directions, where the open model matches or surpasses GPT-4 on individual pairs.
An accessible RASTA pipeline built on an open model is thus already viable for parts of the problem.