Meta-Learning Preferences for Multilingual LLM Alignment
Unequal availability of human preference data across languages poses a significant challenge for aligning large language models in multilingual settings. To address the lack of sufficient data in low-resource language alignment, we propose a meta-learning framework for Reinforcement Learning from Human Feedback and Direct Preference Optimization. By leveraging preference data from other languages, our framework learns a transferable initialization that enables effective adaptation to a target language with minimal data. We provide theoretical guarantees for both the meta-reward modeling and meta-policy optimization settings, and empirically demonstrate the effectiveness of our approach on...
| Authors | Jiaying Lin, Seongho Son, Nam Phuong Tran et al. |
|---|---|
| Primary category | cs.CL |
| Published | 14 July 2026 |
| Last updated at source | 21 July 2026 |
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