Retrieve What Helps, Only When It Helps: Agentic Reinforcement Learning for Selective Retrieval-Augmented Machine Translation

Key points

The model learns a policy that chooses reasoning, retrieval query generation, and translation within a single trajectory.
It is trained with GRPO using final translation quality as reward, so the model itself decides when to retrieve for each input.
To align the retriever with translation quality, the authors built joint Plackett–Luce optimization and translation-quality contrastive training.
On IdiomKB Zh→En, SRAT achieved I5 of 0.731 and MQM of -2.74, beating the strongest baselines.
On RAGtrans, contrastive training raised retrieval accuracy from 56.3% to 70.7% on En→Zh and from 72.6% to 81.4% on En→De.
With a frozen black-box Claude Sonnet 4.6 translator, SRAT-Claude improved RAGtrans En→De MQM by 16.8% over direct translation.

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