Large language models (LLMs) are increasingly capable of generating code, but improving their performance typically requires expensive fine-tuning of all pretrained parameters. A new arXiv paper, CogAdapt, aims to reduce this cost by adapting only a sparse subset of parameters, guided by cognitive principles.
The abstract, which is truncated, establishes the motivation: strong code-generation performance still often relies on costly model adaptation. It does not yet specify which parameters are selected, how cognition informs the selection, or the reported trade-offs in performance versus efficiency.
As the source is a single preprint abstract, the claims are limited to the stated problem and the proposed name. Readers should look to the full paper for experimental results and implementation details.