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Zoom researchers have unveiled a new method for training multilingual AI translation systems that could dramatically cut costs and improve efficiency. Detailed in a paper released on July 29, 2025, the approach—called Reinforcement Learning from Teacher-Model Refinement (RLfR)—eliminates the need for curated preference datasets, which are expensive and time-consuming to create. How RLfR Works Unlike

A recent study by Google and Boston University, presented at the 42nd International Conference on Machine Learning (ICML) in Vancouver, has uncovered a critical flaw in how AI translation benchmarks are evaluated. The research shows that even minor data contamination in training datasets can significantly inflate performance metrics, leading to an overestimation of large language

SAP has integrated large language model (LLM) technology into its cloud-based Translation Hub, expanding its translation capacity to more than 2,800 language pairs across 54 languages. This addition strengthens SAP’s existing machine translation (MT) solutions, giving enterprises greater flexibility in how they localize content. Dual Translation Approach: MT + AI SAP has positioned the new