AI RESEARCH
ReflectMT: Internalizing Reflection for Efficient and High-Quality Machine Translation
arXiv CS.CL
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ArXi:2604.19144v1 Announce Type: new Recent years have witnessed growing interest in applying Large Reasoning Models (LRMs) to Machine Translation (MT). Existing approaches predominantly adopt a "think-first-then-translate" paradigm. Although explicit reasoning trajectories significantly enhance translation quality, they incur prohibitive inference costs and latency. To address these limitations, we propose ReflectMT, a two-stage reflection internalization algorithm for machine translation that employs a "translate-first-think-later" paradigm.