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标题: "Reference-Free Post-Training of Open Large Language Models for Multilingual Machine Translation"
原文链接: "https://arxiv.org/abs/2608.10812"
发布日期: "2026-08-11"
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---

## Computer Science > Computation and Language

## Title:Reference-Free Post-Training of Open Large Language Models for Multilingual Machine Translation

Authors:[Chris Han](https://arxiv.org/search/cs?searchtype=author&query=Han,+C), [Pengzhi Gao](https://arxiv.org/search/cs?searchtype=author&query=Gao,+P), [Pei Fu](https://arxiv.org/search/cs?searchtype=author&query=Fu,+P), [Jian Luan](https://arxiv.org/search/cs?searchtype=author&query=Luan,+J)

[View PDF](https://arxiv.org/pdf/2608.10812) [HTML (experimental)](https://arxiv.org/html/2608.10812v1)

> Abstract:We study reference-free post-training for multilingual machine translation with open large language models. Starting from the supervised-finetuned MiLMMT-46-v0.1 models, we apply Group Relative Policy Optimization (GRPO) with a reward that averages two reference-free quality estimation models and is gated by language identification. We then linearly interpolate the supervised fine-tuning (SFT) and reinforcement learning (RL) model checkpoints to obtain MiLMMT-46-v1.0. Across 46 languages, the resulting models consistently improve translation quality over their SFT counterparts, outperform strong recent open baselines, including Seed-X, HY-MT2, and TranslateGemma, and achieve leading reference-free scores against evaluated proprietary systems such as Google Translate, Gemini 3 Pro, and GPT-5. We further investigate on-policy distillation and find that it reaches, but does not surpass, the quality frontier achieved by RL with checkpoint interpolation. We release the models and code to facilitate future research.

| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| --- | --- |
| Cite as: | [arXiv:2608.10812](https://arxiv.org/abs/2608.10812) \[cs.CL\] |
|  | (or [arXiv:2608.10812v1](https://arxiv.org/abs/2608.10812v1) \[cs.CL\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.10812](https://doi.org/10.48550/arXiv.2608.10812) |

## Submission history

From: Chuang Han \[[view email](https://arxiv.org/show-email/9c236eb8/2608.10812)\]  
**\[v1\]** Tue, 11 Aug 2026 11:30:38 UTC (1,345 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.10812) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
