Low-Rank Adaptation (LoRA) has emerged as a highly efficient framework for finetuning the weights of large foundation models, and has become the go-to method for data-driven customization of LLMs. Despite the promise of highly customized behaviors and capabilities, switching between relevant LoRAs in a multiturn setting is highly inefficient, as the key-value (KV) cache of the entire turn history must be recomputed with the LoRA weights before generation can begin. To address this problem, we propose Activated LoRA (aLoRA), which modifies the LoRA framework to only adapt weights for the tokens in the sequence \emph{after} the aLoRA is invoked. This change crucially allows aLoRA to accept the base model's KV cache of the input string, meaning that aLoRA can be instantly activated whenever needed in a chain without recomputing the cache. This enables building what we call \emph{intrinsics}, i.e. highly specialized models invoked to perform well-defined operations on portions of an input chain or conversation that otherwise uses the base model by default. We use aLoRA to train a set of intrinsics models, demonstrating competitive accuracy with standard LoRA while achieving significant inference benefits.
View on arXiv@article{greenewald2025_2504.12397,
title={ Activated LoRA: Fine-tuned LLMs for Intrinsics },
author={ Kristjan Greenewald and Luis Lastras and Thomas Parnell and Vraj Shah and Lucian Popa and Giulio Zizzo and Chulaka Gunasekara and Ambrish Rawat and David Cox },
journal={arXiv preprint arXiv:2504.12397},
year={ 2025 }
}