@inproceedings{lai-etal-2026-aligndiff,
title = "{A}lign{D}iff: Exploiting Model-Intrinsic Information for Better Preference Data Selection",
author = "Lai, Peng and
Zhu, He and
Ruan, Zhiwen and
Zhang, Dongdong and
Chen, Yun and
Li, Peng and
Wei, Furu and
Liu, Yang and
Chen, Guanhua",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.85/",
pages = "1734--1755",
ISBN = "979-8-89176-462-0",
abstract = "Aligning large language models with human preferences remains a challenge, primarily due to the critical role of preference data quality in effective alignment. Existing datasets are frequently plagued by inherent noise and distribution shifts, which inherently limit model performance. To bridge this gap, we propose AlignDiff, a preference data filtering framework driven by intrinsic model signals. AlignDiff first identifies samples with clear preferences using both positive and inverse signals, then prioritizes the more challenging samples based on the average negative log-likelihood gap, encouraging the model to learn richer information from them. AlignDiff is evaluated on two widely used model families (LLaMA and Qwen) and three benchmarks widely adopted in the alignment community (AlpacaEval 2.0, Arena-Hard, and MT-Bench). Across all settings, it consistently outperforms seven strong baselines. We conduct comprehensive ablation studies to validate the effectiveness of AlignDiff, and further show that difficulty-based curriculum learning improves model performance."
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<abstract>Aligning large language models with human preferences remains a challenge, primarily due to the critical role of preference data quality in effective alignment. Existing datasets are frequently plagued by inherent noise and distribution shifts, which inherently limit model performance. To bridge this gap, we propose AlignDiff, a preference data filtering framework driven by intrinsic model signals. AlignDiff first identifies samples with clear preferences using both positive and inverse signals, then prioritizes the more challenging samples based on the average negative log-likelihood gap, encouraging the model to learn richer information from them. AlignDiff is evaluated on two widely used model families (LLaMA and Qwen) and three benchmarks widely adopted in the alignment community (AlpacaEval 2.0, Arena-Hard, and MT-Bench). Across all settings, it consistently outperforms seven strong baselines. We conduct comprehensive ablation studies to validate the effectiveness of AlignDiff, and further show that difficulty-based curriculum learning improves model performance.</abstract>
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%0 Conference Proceedings
%T AlignDiff: Exploiting Model-Intrinsic Information for Better Preference Data Selection
%A Lai, Peng
%A Zhu, He
%A Ruan, Zhiwen
%A Zhang, Dongdong
%A Chen, Yun
%A Li, Peng
%A Wei, Furu
%A Liu, Yang
%A Chen, Guanhua
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F lai-etal-2026-aligndiff
%X Aligning large language models with human preferences remains a challenge, primarily due to the critical role of preference data quality in effective alignment. Existing datasets are frequently plagued by inherent noise and distribution shifts, which inherently limit model performance. To bridge this gap, we propose AlignDiff, a preference data filtering framework driven by intrinsic model signals. AlignDiff first identifies samples with clear preferences using both positive and inverse signals, then prioritizes the more challenging samples based on the average negative log-likelihood gap, encouraging the model to learn richer information from them. AlignDiff is evaluated on two widely used model families (LLaMA and Qwen) and three benchmarks widely adopted in the alignment community (AlpacaEval 2.0, Arena-Hard, and MT-Bench). Across all settings, it consistently outperforms seven strong baselines. We conduct comprehensive ablation studies to validate the effectiveness of AlignDiff, and further show that difficulty-based curriculum learning improves model performance.
%U https://aclanthology.org/2026.findings-emnlp.85/
%P 1734-1755
Markdown (Informal)
[AlignDiff: Exploiting Model-Intrinsic Information for Better Preference Data Selection](https://aclanthology.org/2026.findings-emnlp.85/) (Lai et al., Findings 2026)
ACL
- Peng Lai, He Zhu, Zhiwen Ruan, Dongdong Zhang, Yun Chen, Peng Li, Furu Wei, Yang Liu, and Guanhua Chen. 2026. AlignDiff: Exploiting Model-Intrinsic Information for Better Preference Data Selection. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 1734–1755, Budapest, Hungary. Association for Computational Linguistics.