QF: Quick Feedforward AI Model Training without Gradient Back Propagation

2025-07-06

AI Summary

This work addresses the challenge of efficiently injecting instruction knowledge into Transformer models without gradient backpropagation, while preserving pre-trained capabilities and minimizing parameter updates. To this end, we propose QF (Query-based Fast learning), a feedforward rapid adaptation framework. QF computes weight updates via closed-form analytical solutions, enabling single-step, backpropagation-free knowledge consolidation and supporting unified training-inference deployment. Its core contribution is the first demonstration of fully backpropagation-free parameter adaptation in Transformers—achieving performance parity with conventional fine-tuning while reducing GPU memory consumption and training time by over 50%. The approach is both biologically inspired and highly resource-efficient. All code and models are publicly released.

Technology Category

• Search and Optimization: Learning to Search
• Natural Language Processing: Learning & Optimization for NLP
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Efficient knowledge transfer without gradient backpropagation
• Closed-form updates preserving prior model knowledge
• Unified training and inference for resource efficiency

Innovation

• Quick Feedforward Learning updates weights without backpropagation
• Closed-form updates minimize parameter modification
• Same runtime environment for training and inference