Parameter Adaptation

Parameter adaptation focuses on efficiently modifying pre-trained models for new tasks, minimizing computational cost and maximizing performance. Current research emphasizes techniques like Low-Rank Adaptation (LoRA) and its variants, exploring both weight adjustments and modifications to intermediate activations, as well as adaptation strategies for nearest-neighbor models and reinforcement learning-based approaches for automated hyperparameter tuning. These advancements are crucial for deploying large models in resource-constrained environments and improving the robustness and adaptability of machine learning systems across diverse applications.