@inproceedings{shen-etal-2026-pegrl,
    title = "{PEGRL}: Improving Machine Translation by Post-Editing Guided Reinforcement Learning",
    author = "Shen, Yunzhi  and
      Zhou, Hao  and
      Huang, Xin  and
      Han, Xue  and
      Feng, Junlan  and
      Huang, Shujian",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.851/",
    pages = "17225--17242",
    ISBN = "979-8-89176-395-1",
    abstract = "Reinforcement learning (RL) has shown strong promise for LLM-based machine translation, with recent methods such as GRPO demonstrating notable gains; nevertheless, translation-oriented RL remains challenged by high-variance policy gradients induced by Monte Carlo baselines, as well as a large trajectory space that favors global exploration over fine-grained local optimization. We introduce \textbf{PEGRL}, a \textit{two-stage} RL framework that uses post-editing as an auxiliary task to stabilize training and guide overall optimization. At each step, translation outputs are sampled to construct post-editing inputs, enabling lower-variance gradients from the post-editing task to propagate through the entire framework while jointly supporting both global exploration and fine-grained local optimization. A task-specific weighting scheme further emphasizes the post-editing gradient, producing a biased yet more sample-efficient estimator. Experiments on English$\to$Finnish, English$\to$Turkish, and English$\leftrightarrow$Chinese show consistent gains over RL baselines, and for English$\to$Turkish, performance on COMETKiwi is comparable to advanced LLM-based systems (DeepSeek-V3.2). Our code and a set of representative pretrained models are publicly available at \url{https://github.com/NJUNLP/peg-rl} and \url{https://huggingface.co/collections/DGME/pegrl}."
}