Exploring LoRA as a Dynamic Neural Network Layer for Efficient LLM Adaptation

Author(s): Shenggang Li

Originally published on Towards AI.

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LLMs 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 isn’t 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.

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Published via Towards AI