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arXiv · 2508.08466

Enhancing Small LLM Alignment through Margin-Based Objective Modifications under Resource Constraints

Daren Yao·Jinsong Yuan·Ruike Chen
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Abstract

Small large language models (LLMs) often face difficulties in aligning output to human preferences, particularly when operating under severe performance gaps. In this work, we propose two lightweight DPO-based variants -- Adaptive Margin-Sigmoid Loss and APO-hinge-zero -- to better address underperformance scenarios by introducing margin-based objectives and selective update mechanisms. Our APO-hinge-zero method, which combines hinge-induced hard-example mining with the chosen-focused optimization of APO-zero, achieves strong results. In AlpacaEval, APO-hinge-zero improves the win rate by +2.0 points and the length-controlled win rate by +1.4 points compared to the APO-zero baseline. In MT-Bench, our methods maintain competitive performance in diverse categories, particularly excelling in STEM and Humanities tasks. These results demonstrate that simple modifications to preference-based objectives can significantly enhance small LLM alignment under resource constraints, offering a practical path toward more efficient deployment.

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Daren Yao, Jinsong Yuan, Ruike Chen. 2025-08-11. Enhancing Small LLM Alignment through Margin-Based Objective Modifications under Resource Constraints. https://arxiv.org/abs/2508.08466

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