Beyond Single-Reward: Multi-Pair, Multi-Perspective Preference Optimization for Machine Translation

• URL: http://arxiv.org/abs/2510.13434v1
• Date: Wed, 15 Oct 2025 11:30:49 GMT
• Title: Beyond Single-Reward: Multi-Pair, Multi-Perspective Preference Optimization for Machine Translation
• Authors: Hao Wang, Linlong Xu, Heng Liu, Yangyang Liu, Xiaohu Zhao, Bo Zeng, Liangying Shao, Longyue Wang, Weihua Luo, Kaifu Zhang,
• Abstract summary: We introduce M2PO: Multi-Pair, Multi-Perspective Preference Optimization.<n>Our framework integrates a multi-perspective reward engine that creates a more robust signal.<n>On challenging WMT21-22 benchmarks, M2PO substantially outperforms existing preference optimization methods.
• Score: 44.04325848740683
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Direct Preference Optimization (DPO) is a powerful paradigm for aligning Large Language Models (LLMs) to human preferences in Machine Translation (MT), but current methods are hindered by two fundamental challenges: (1) flawed reward signals from Quality Estimation (QE) models that overlook critical errors like translation hallucination, and (2) inefficient data utilization that discards valuable learning signals by selecting only a single win-loss pair. To address these limitations, we introduce M^2PO: Multi-Pair, Multi-Perspective Preference Optimization. Our framework integrates a multi-perspective reward engine that creates a more robust signal by combining two key viewpoints: a new hallucination penalty for factuality, and an innovative dynamic quality score that adaptively fuses external evaluations with the model's own evolving judgment. This is synergistically paired with a multi-pair construction strategy that systematically creates a comprehensive set of preference pairs from the entire pool of translation candidates. This synergistic approach ensures the model learns from a richer spectrum of quality trade-offs, leading to more robust and faithful translations. On challenging WMT21-22 benchmarks, M^2PO substantially outperforms existing preference optimization methods and demonstrates highly competitive performance against leading proprietary LLMs.

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