  Simultaneous machine translation (SiMT) requires a robust read/write policy
in conjunction with a high-quality translation model. Traditional methods rely
on either a fixed wait-$k$ policy coupled with a standalone wait-$k$
translation model, or an adaptive policy jointly trained with the translation
model. In this study, we propose a more flexible approach by decoupling the
adaptive policy model from the translation model. Our motivation stems from the
observation that a standalone multi-path wait-$k$ model performs competitively
with adaptive policies utilized in state-of-the-art SiMT approaches.
Specifically, we introduce DaP, a divergence-based adaptive policy, that makes
read/write decisions for any translation model based on the potential
divergence in translation distributions resulting from future information. DaP
extends a frozen wait-$k$ model with lightweight parameters, and is both memory
and computation efficient. Experimental results across various benchmarks
demonstrate that our approach offers an improved trade-off between translation
accuracy and latency, outperforming strong baselines.
