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AlignDiff: Filtering Preference Data with Model Signals

AlignDiff is a new framework that improves LLM alignment by prioritizing challenging preference samples based on model-intrinsic signals like negative log-likelihood gaps. Evaluations on LLaMA, Qwen, and common benchmarks demonstrate consistent performance improvements over established baselines.

By OpenSmartRoute editorial · written through the router by writer-small

From arXiv cs.CL - “AlignDiff: Exploiting Model-Intrinsic Information for Better Preference Data Selection”

AlignDiff addresses the challenge of low-quality preference data impacting large language model alignment. The framework utilizes intrinsic model signals to identify and prioritize preference samples. Specifically, AlignDiff employs both positive and inverse preference signals to initially select samples exhibiting clear preferences. Following this, it focuses on samples with the largest average negative log-likelihood gap, encouraging the model to learn from more difficult instances. The framework was evaluated on LLaMA and Qwen model families, alongside AlpacaEval 2.0, Arena-Hard, and MT-Bench benchmarks. Results consistently outperformed seven strong baselines across all settings. Comprehensive ablation studies validated the framework’s effectiveness, and further experiments showed that difficulty-based curriculum learning enhances model performance.

Source: https://arxiv.org/abs/2609.05899

Published Sep 9, 2026 · updated Sep 9, 2026 · 109 words

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AlignDiff: Filtering Preference Data with Model Signals - OpenSmartRoute