Preference Optimization for Robust Instruction Following in Parameter-Efficient Language Models
Keywords:
preference optimization; parameter-efficient fine-tuning; instruction following; robustness; alignment; governance; sustainable AI infrastructure; fairnessAbstract
Instruction following has become a central criterion for evaluating whether large language models can be deployed safely and usefully in real-world socio-technical systems. Yet the dominant path to strong instruction following often depends on full-scale fine-tuning and reinforcement learning from human feedback, both of which impose substantial computational, operational, and governance burdens. Parameter-efficient language models offer a pragmatic alternative by adapting only a small fraction of model parameters, but their smaller adaptation footprint can intensify known weaknesses in preference alignment, including brittle generalization, uneven robustness across domains, and hidden fairness failures. This paper examines preference optimization as a system-level strategy for robust instruction following in parameter-efficient language models. It synthesizes research on adapter-based tuning, low-rank adaptation, prompt and prefix tuning, direct preference optimization, and human-preference reinforcement learning, then connects these methods to deployment concerns such as serving infrastructure, evaluation validity, sustainability, auditability, and policy oversight. The analysis emphasizes structural trade-offs rather than isolated algorithmic gains. Parameter-efficient preference optimization can reduce training and inference costs, enable distributed adaptation, and support domain-specific governance, but it can also concentrate risk in small trainable modules, obscure responsibility across model layers, and produce misleading benchmark improvements. The paper argues that robust instruction following in parameter-efficient settings requires layered evaluation, preference data governance, deployment-aware robustness testing, and transparent reporting of adaptation scope. It concludes that preference optimization should be treated as part of an infrastructure and accountability regime, not merely as a training technique.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
