RLSR
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Rewrite to Translate, Translate to Reward: Reinforcement Learning for Source Rewriting in Machine Translation
arXiv:2606.08011v1 Announce Type: new Abstract: Although directly prompting off-the-shelf Large Language Models (LLMs) to generate meaning-preserving source rewrites can effectively enhance Machine Translation (MT) quality, doing so requires manually tuning prompts for different MT models. In this work, we propose RLSR (Reinforcement Learning for Source Rewriting), a novel RL-based framework for training a source rewriting model without tuning prompts for each MT model.