QF: Quick Feedforward AI Model Training without Gradient Back Propagation
This work addresses the resource inefficiency of training AI models, particularly for transformer-based systems, by proposing a brain-like paradigm that could benefit developers and researchers seeking faster and more sustainable model adaptation.
The paper tackles the problem of inefficient gradient-based fine-tuning by introducing Quick Feedforward (QF) Learning, a framework that transfers instruction-derived knowledge into transformer model weights using feedforward activations without gradient backpropagation, resulting in closed-form updates that require minimal parameter changes and preserve prior knowledge.
We propose Quick Feedforward (QF) Learning, a novel knowledge consolidation framework for transformer-based models that enables efficient transfer of instruction derived knowledge into model weights through feedforward activations without any gradient back propagation. Unlike traditional finetuning, QF updates are computed in closed form, require minimal parameter modification, and preserve prior knowledge. Importantly, QF allows models to train and infer within the same runtime environment, making the process more resource efficient and closely aligned with how the human brain operates. Code and models are open sourced on GitHub. I hope QF Learning inspires a more efficient and brain-like paradigm for AI systems.