Dynamic Structured Gating for Parameter-Efficient Alignment of Large Pretrained Models

Abstract

This paper proposes a large model alignment algorithm based on parameter-efficient fine-tuning and structured adapter gating to address the difficulty of balancing performance and efficiency under resource constraints and complex environments. The method introduces low-rank updates and gating control modules into the backbone of large models, enabling fine-grained selection of feature flows and suppression of irrelevant information through the dynamic adjustment of sparse adapters. Compared with traditional full fine-tuning, it significantly reduces training and inference costs while maintaining high alignment quality and robustness across diverse environments. Systematic experiments under hyperparameter sensitivity, environmental constraints, and data noise show that the method achieves superior results on key metrics such as ROC-AUC, F1-Score, and parameter efficiency, with strong stability and adaptability in semantic noise and conflict feedback scenarios. Additional experiments under computational and memory limits confirm the flexibility of structured gating in resource utilization, while results under reduced training samples and sparse labels highlight its robustness in weakly supervised settings. Overall, the proposed approach balances accuracy and efficiency in alignment, providing a feasible technical path for deploying large models under complex conditions.