Automatic Stability and Recovery for Neural Network Training

2026-01-24

AI Summary

This work addresses the vulnerability of neural network training to rare yet severe unstable updates, which can cause irreversible divergence or subtle performance degradation—issues that existing optimizers fail to detect or mitigate at runtime. The authors model the optimization process as a controlled stochastic process and introduce the first runtime stability framework that operates without modifying the underlying optimizer. By leveraging secondary signals such as validation probes, the framework automatically detects instability and triggers lightweight interventions grounded in control theory. Designed for memory-constrained settings, the approach offers low computational overhead, broad compatibility with standard optimizers, and theoretical guarantees of bounded degradation and recovery, effectively preventing training collapse and performance deterioration.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Machine Learning: Optimization
• Computer Vision: Learning & Optimization for CV

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• 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

Training modern neural networks is increasingly fragile, with rare but severe destabilizing updates often causing irreversible divergence or silent performance degradation. Existing optimization methods primarily rely on preventive mechanisms embedded within the optimizer, offering limited ability to detect and recover from instability once it occurs. We introduce a supervisory runtime stability framework that treats optimization as a controlled stochastic process. By isolating an innovation signal derived from secondary measurements, such as validation probes, the framework enables automatic detection and recovery from destabilizing updates without modifying the underlying optimizer. We provide theoretical runtime safety guarantees that formalize bounded degradation and recovery. Our implementation incurs minimal overhead and is compatible with memory-constrained training settings.

Problem

• neural network training
• training instability
• automatic recovery
• optimization
• stability

Innovation

• runtime stability
• automatic recovery
• stochastic optimization
• validation probes
• bounded degradation