aiqbench analysis
AlignDiff addresses the critical bottleneck of noisy preference data in large language model alignment by leveraging intrinsic model signals to filter high-quality samples. By prioritizing challenging instances based on negative log-likelihood gaps, this framework effectively reduces distribution shifts and enhances learning efficiency across major model families like LLaMA and Qwen. The method demonstrates that difficulty-based curriculum learning is a viable strategy for improving performance on standard alignment benchmarks without requiring external supervision.
AlignDiff: Exploiting Model-Intrinsic Information for Better Preference Data Selection
arXiv:2609.05899v1 Announce Type: new 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: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.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2609.05899 [cs.CL]
(or arXiv:2609.05899v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.05899 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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