QF: Quick Feedforward AI Model Training without Gradient Back Propagation

TL;DR

QF Learning introduces a gradient-free, efficient method for training transformer models by directly updating weights through feedforward activations, reducing resource use and mimicking brain-like learning.

Contribution

The paper presents a novel feedforward-based training framework that eliminates gradient backpropagation, enabling efficient, resource-friendly model updates and inference.

Findings

QF achieves comparable performance to traditional fine-tuning.

Training and inference are performed within the same runtime environment.

The method requires minimal parameter modifications.

Abstract

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.