@inproceedings{li-etal-2026-congrad,
title = "{CONGRAD}: Conflicting Gradient Filtering for Multilingual Preference Alignment",
author = "Li, Jiangnan and
Vu, Thuy-Trang and
Herold, Christian and
Tebbifakhr, Amirhossein and
Khadivi, Shahram and
Haffari, Gholamreza",
editor = "Demberg, Vera and
Inui, Kentaro and
Marquez, Llu{\'i}s",
booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.eacl-long.299/",
pages = "6371--6387",
ISBN = "979-8-89176-380-7",
abstract = "Naive joint training of large language models (LLMs) for multilingual preference alignment can suffer from negative interference. This is a known issue in multilingual training, where conflicting objectives degrade overall performance. However, the impact of this phenomenon in the context of multilingual preference alignment remains largely underexplored. To address this issue, we propose ConGrad, an effective and scalable filtering method that mitigates this interference by identifying and selecting preference samples that exhibit high cross-lingual affinity. Based on principles of multi-objective optimization, our approach computes an aggregated, cross-lingually beneficial gradient direction and uses this to filter for samples whose individual gradients align with this consensus direction. To ensure scalability for LLMs, we incorporate a sublinear gradient compression strategy that reduces memory overhead during gradient accumulation. We integrate ConGrad into a self-rewarding framework and evaluate on LLaMA3-8B and Gemma2-2B across 10 languages. Results show that ConGrad consistently outperforms strong baselines in both seen and unseen languages, with minimal alignment tax."
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<abstract>Naive joint training of large language models (LLMs) for multilingual preference alignment can suffer from negative interference. This is a known issue in multilingual training, where conflicting objectives degrade overall performance. However, the impact of this phenomenon in the context of multilingual preference alignment remains largely underexplored. To address this issue, we propose ConGrad, an effective and scalable filtering method that mitigates this interference by identifying and selecting preference samples that exhibit high cross-lingual affinity. Based on principles of multi-objective optimization, our approach computes an aggregated, cross-lingually beneficial gradient direction and uses this to filter for samples whose individual gradients align with this consensus direction. To ensure scalability for LLMs, we incorporate a sublinear gradient compression strategy that reduces memory overhead during gradient accumulation. We integrate ConGrad into a self-rewarding framework and evaluate on LLaMA3-8B and Gemma2-2B across 10 languages. Results show that ConGrad consistently outperforms strong baselines in both seen and unseen languages, with minimal alignment tax.</abstract>
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%0 Conference Proceedings
%T CONGRAD: Conflicting Gradient Filtering for Multilingual Preference Alignment
%A Li, Jiangnan
%A Vu, Thuy-Trang
%A Herold, Christian
%A Tebbifakhr, Amirhossein
%A Khadivi, Shahram
%A Haffari, Gholamreza
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-380-7
%F li-etal-2026-congrad
%X Naive joint training of large language models (LLMs) for multilingual preference alignment can suffer from negative interference. This is a known issue in multilingual training, where conflicting objectives degrade overall performance. However, the impact of this phenomenon in the context of multilingual preference alignment remains largely underexplored. To address this issue, we propose ConGrad, an effective and scalable filtering method that mitigates this interference by identifying and selecting preference samples that exhibit high cross-lingual affinity. Based on principles of multi-objective optimization, our approach computes an aggregated, cross-lingually beneficial gradient direction and uses this to filter for samples whose individual gradients align with this consensus direction. To ensure scalability for LLMs, we incorporate a sublinear gradient compression strategy that reduces memory overhead during gradient accumulation. We integrate ConGrad into a self-rewarding framework and evaluate on LLaMA3-8B and Gemma2-2B across 10 languages. Results show that ConGrad consistently outperforms strong baselines in both seen and unseen languages, with minimal alignment tax.
%U https://aclanthology.org/2026.eacl-long.299/
%P 6371-6387
Markdown (Informal)
[CONGRAD: Conflicting Gradient Filtering for Multilingual Preference Alignment](https://aclanthology.org/2026.eacl-long.299/) (Li et al., EACL 2026)
ACL
- Jiangnan Li, Thuy-Trang Vu, Christian Herold, Amirhossein Tebbifakhr, Shahram Khadivi, and Gholamreza Haffari. 2026. CONGRAD: Conflicting Gradient Filtering for Multilingual Preference Alignment. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6371–6387, Rabat, Morocco. Association for Computational Linguistics.