A new translation method gives you the high quality of slow-thinking AI models at the lightning speed of fast-thinking ones.
April 23, 2026
Original Paper
ReflectMT: Internalizing Reflection for Efficient and High-Quality Machine Translation
arXiv · 2604.19144
The Takeaway
ReflectMT internalizes the reflection process of reasoning models so they do not have to output a visible chain-of-thought. This allows the AI to produce near-perfect translations in a single pass, saving over 94% on token costs. We previously thought that high-quality reasoning required a long, visible internal monologue. This research proves that a model can think internally without slowing down or wasting tokens. It makes elite-level AI reasoning affordable for real-time applications like live translation and voice assistants.
From the abstract
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-late