Adaptive Learning Guided by Bias-Noise-Alignment Diagnostics
Akash Samanta, Sheldon Williamson
TL;DR
The paper addresses instability in adaptive learning under nonstationary conditions by moving beyond gradient-centric updates to a diagnostic-driven framework that explicitly models error evolution. It introduces a bias-noise-alignment decomposition computed online from loss or TD-error trajectories, and demonstrates its unifying applicability by instantiating it for supervised optimization (HSAO), actor-critic RL (HED-RL), and meta-learning (MLLP) with bounded updates and descent-style stability under standard smoothness. The key contributions are the formal diagnostics, their model-agnostic integration across learning paradigms, and the empirical demonstrations of stability and interpretability via ablations. This approach provides a principled, lightweight foundation for reliable learning in dynamic environments and offers a bridge between optimization, control, and meta-learning with potential for broader deployment in safety-critical settings.
Abstract
Learning systems deployed in nonstationary and safety-critical environments often suffer from instability, slow convergence, or brittle adaptation when learning dynamics evolve over time. While modern optimization, reinforcement learning, and meta-learning methods adapt to gradient statistics, they largely ignore the temporal structure of the error signal itself. This paper proposes a diagnostic-driven adaptive learning framework that explicitly models error evolution through a principled decomposition into bias, capturing persistent drift; noise, capturing stochastic variability; and alignment, capturing repeated directional excitation leading to overshoot. These diagnostics are computed online from lightweight statistics of loss or temporal-difference error trajectories and are independent of model architecture or task domain. We show that the proposed bias-noise-alignment decomposition provides a unifying control backbone for supervised optimization, actor-critic reinforcement learning, and learned optimizers. Building on this framework, we derive diagnostic-driven instantiations including a stabilized supervised optimizer, a diagnostic-regulated actor-critic scheme, and a diagnostic-conditioned learned optimizer. Under standard smoothness assumptions, we establish bounded effective updates and stability properties for all cases. Representative diagnostic illustrations in actor-critic learning highlight how the proposed signals modulate adaptation in response to temporal-difference error structure. Overall, this work elevates error evolution to a first-class object in adaptive learning and provides an interpretable, lightweight foundation for reliable learning in dynamic environments.