SAL: Selective Adaptive Learning for Backpropagation-Free Training with Sparsification

2026-01-29

AI Summary

Standard backpropagation suffers from biologically implausible assumptions—such as weight symmetry—and gradient interference caused by dense activation. This work proposes Selective Adaptive Learning (SAL), a novel training framework that circumvents explicit weight symmetry by partitioning the parameter space into sample-dependent, mutually exclusive regions and activating only relevant subsets during forward and backward passes. SAL integrates adaptive sparsification, selective parameter updates, and a refined feedback alignment strategy to deliver stable and efficient learning. The method achieves convergence speed and accuracy comparable to backpropagation across ten standard benchmarks, scales to models with up to 128 layers and billions of parameters, and demonstrates strong numerical stability and scalability without relying on symmetric weight transport.

Technology Category

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

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Standard deep learning relies on Backpropagation (BP), which is constrained by biologically implausible weight symmetry and suffers from significant gradient interference within dense representations. To mitigate these bottlenecks, we propose Selective Adaptive Learning (SAL), a training method that combines selective parameter activation with adaptive area partitioning. Specifically, SAL decomposes the parameter space into mutually exclusive, sample-dependent regions. This decoupling mitigates gradient interference across divergent semantic patterns and addresses explicit weight symmetry requirements through our refined feedback alignment. Empirically, SAL demonstrates competitive convergence rates, leading to improved classification performance across 10 standard benchmarks. Additionally, SAL achieves numerical consistency and competitive accuracy even in deep regimes (up to 128 layers) and large-scale models (up to 1B parameters). Our approach is loosely inspired by biological learning mechanisms, offering a plausible alternative that contributes to the study of scalable neural network training.

Problem

• Backpropagation
• weight symmetry
• gradient interference
• dense representations
• biological plausibility

Innovation

• Selective Adaptive Learning
• Backpropagation-Free Training
• Gradient Interference Mitigation
• Feedback Alignment
• Sparsification