Adaptive Large Language Model Fine-Tuning via LoRA-Based Low-Rank Modulation and PPO Reinforcement Learning

In recent years, large language models (LLMs) have exhibited impressive performance across a broad range of natural language processing tasks. However, their adaptation to domain-specific applications remains constrained by the prohibitive computational cost of fine-tuning. Conventional approaches typically require updating the full set of parameters, which becomes infeasible for models of substantial size. Although Low-Rank Adaptation (LoRA) offers a parameter-efficient alternative by freezing pre-trained weights and introducing trainable low-rank matrices, most existing implementations rely on static rank assignments, limiting their adaptability to dynamic task complexities and undermining generalization capabilities. To address this limitation, we propose a reinforcement learning-based framework that enables dynamic rank adaptation. We design a composite reward function that jointly considers task performance, parameter efficiency, and training stability. Additionally, we introduce a smooth rank transition mechanism based on truncated singular value decomposition and momentum-weighted interpolation. The overall training procedure alternates between exploration and exploitation phases, allowing the policy to continuously refine rank allocation strategies based on real-time feedback. This study shows the feasibility of integrating reinforcement learning into parameter-efficient fine-tuning, offering a practical framework to adapt LLMs in resource-constrained settings while enhancing task-specific flexibility and responsiveness.