---
title: "推理先于翻译：结构化推理增强法律机器翻译"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.19181v1"
permalink: "https://feed.astralor.com/items/itm_843e322486a6f3462ac80cc1"
published: "2026-07-21T15:15:56.000Z"
score: 77
channel: "tech"
tags: ["Gemma 3","Qwen3.5","强化学习","法律机器翻译","瑞士","监督微调"]
---

# 推理先于翻译：结构化推理增强法律机器翻译

## AI 点评

> 这篇论文特别值得关注，因为它在法律AI这个垂直领域做了非常扎实的对照实验，没有盲目吹捧大模型，而是证明了小模型+强化学习也能达到接近前沿的效果。对做AI落地或法律翻译产品的团队来说，这是立刻就能参考的技术路线。

## 速览

本文对比监督微调与强化学习等范式在小语言模型法律翻译中的效果，以瑞士多语言法规为测试床。结果显示：强化学习显著提升小模型翻译质量，超越监督微调，但模型增大时重训练收益递减；增强后的小模型性能接近前沿推理模型，但仍有差距。代码与模型已开源。

## 关键点

- 监督微调与强化学习在法律机器翻译中的对比实验
- 强化学习（可验证奖励）显著提升小模型翻译质量
- 瑞士多语言法规系统作为独特且挑战性的测试床
- 模型规模增大时重训练收益递减，小模型经增强后接近前沿推理模型

## 全文

Neural machine translation (NMT) in the legal domain is a linguistically and conceptually demanding task, primarily due to the complexity of legal language and the high level of precision it requires. The recent emergence of reasoning-capable language models opens new possibilities for tackling such challenges. They add to a set of other previously proposed techniques to enhance the translation quality, which includes supervised fine-tuning and reinforcement learning. In this work, we perform a comparison between these various approaches. More particularly, we evaluate small language models such as Qwen3.5 4B, Qwen3.5 9B, and Gemma 3 12B enhanced with various re-training paradigms and compare their performances against frontier reasoning models. We focus on the Swiss legal system, which -- with its unique multilingual statutes -- offers a particularly challenging testbed for reasoning-augmented models. Our results show that the quality of small ``base'' models can be greatly enhanced, and that reinforcement learning with verifiable rewards can be applied to NMT in the legal domain and surpasses the translation quality of supervised fine-tuning. The performance of enhanced small models is close to the one of state-of-the-art reasoning models yet remains inferior. We also note that re-training paradigms yield diminishing returns as model size increase. The code and models are publicly available at https://github.com/aixiuxiuxiu/Legal-MT-SFT-RL.

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引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_843e322486a6f3462ac80cc1