LatestMachine LearningExploring LoRA as a Dynamic Neural Network Layer for Efficient LLM Adaptation 0 like February 26, 2025Share this postAuthor(s): Shenggang Li Originally published on Towards AI. This member-only story is on us. Upgrade to access all of Medium.Photo by Jakub erdzicki on UnsplashLLMs need constant updates legal AI must learn new laws, finance chatbots need fresh market data, and medical models should adapt to new research. But traditional fine-tuning is expensive. LoRA helps, but most versions are static, using a fixed rank for updates. I propose a smarter approach: a dynamic LoRA that adjusts rank based on data complexity, making fine-tuning more efficient.I start with full fine-tuning, move to LoRA theory, and introduce Rank-1 Sum LoRA. Instead of one fixed low-rank matrix, I sum multiple rank-1 updates and prune unnecessary ones, making training smarter and more efficient:This lets me selectively activate only the most useful updates, pruning the rest. By leveraging retrieval confidence or gradient signals, LoRA can learn more intelligently.Traditionally, fine-tuning an LLM involved unfreezing all weights in a pre-trained model, a process known as full fine-tuning. While this isnt the primary focus of this paper, understanding it provides valuable context for how LoRA fine-tuning operates.Suppose I have a neural network NN1 that was already trained on some large dataset. Mathematically, it has a parameter set:where n is the total number of parameters (weights, Read the full blog for free on Medium.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post