Less is More: Efficient Weight Farcasting with 1-Layer Neural Network

2025-05-05

AI Summary

To address the high computational cost and poor scalability of iterative optimization in large-model training, this work pioneers a paradigm shift: modeling weight evolution as a time-series forecasting task—bypassing conventional stepwise gradient updates. Methodologically, it employs a single-layer neural network to directly predict the weight states at both ends of the optimization trajectory; introduces a trajectory-aware regularizer specifically designed for weight dynamics to enhance long-horizon prediction accuracy; and maintains an exceptionally lightweight architecture with negligible overhead. Evaluated on real-world models including DistilBERT, the approach achieves significantly reduced weight prediction error, accelerates training by multiple-fold, and preserves convergence quality. The core contribution is the proposal of “weight forecasting”—a novel, scalable, and low-overhead paradigm for efficient large-model training.

Technology Category

• Natural Language Processing: Learning & Optimization for NLP
• Machine Learning: Optimization
• Search and Optimization: Learning to Search

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Addressing the computational challenges inherent in training large-scale deep neural networks remains a critical endeavor in contemporary machine learning research. While previous efforts have focused on enhancing training efficiency through techniques such as gradient descent with momentum, learning rate scheduling, and weight regularization, the demand for further innovation continues to burgeon as model sizes keep expanding. In this study, we introduce a novel framework which diverges from conventional approaches by leveraging long-term time series forecasting techniques. Our method capitalizes solely on initial and final weight values, offering a streamlined alternative for complex model architectures. We also introduce a novel regularizer that is tailored to enhance the forecasting performance of our approach. Empirical evaluations conducted on synthetic weight sequences and real-world deep learning architectures, including the prominent large language model DistilBERT, demonstrate the superiority of our method in terms of forecasting accuracy and computational efficiency. Notably, our framework showcases improved performance while requiring minimal additional computational overhead, thus presenting a promising avenue for accelerating the training process across diverse tasks and architectures.

Problem

• Efficient training of large-scale deep neural networks
• Reducing computational overhead in weight forecasting
• Improving forecasting accuracy with minimal resources

Innovation

• Uses 1-layer neural network for weight forecasting
• Leverages initial and final weight values only
• Introduces tailored regularizer for enhanced performance