Rewrite to Translate, Translate to Reward: Reinforcement Learning for Source Rewriting in Machine Translation
Prior work has explored prompting large language models (LLMs) to rewrite source text before translation, with the goal of improving machine translation (MT) quality. However, we find that such prompt-based rewriting can degrade translation quality rather than enhance it, particularly when smaller LLMs, such as 4B-parameter models, are used. We argue that this limitation stems from the difficulty of controlling rewriting behavior through natural-language prompts alone: a rewrite is useful only if it leads to a better downstream translation, yet existing prompt-based methods do not explicitly optimize for this signal. To address this issue, we propose \textbf{RLSR} (\textbf{R}einforcement ...
| Authors | Boxuan Lyu, Haiyue Song, Zhi Qu et al. |
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| Primary category | cs.CL |
| Published | 6 June 2026 |
| Last updated at source | 2 August 2026 |
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