Learning Can Converge Stably to the Wrong Belief under Latent Reliability

2026-03-22

AI Summary

When feedback reliability is unobservable, learning systems are prone to converge stably to incorrect solutions due to their inability to discern the trustworthiness of individual feedback signals. To address this challenge, this work proposes the Monitor-Trust-Regulator (MTR) framework, which uniquely treats the learning dynamics themselves as an informative source about feedback reliability. By integrating dynamic monitoring, trust modeling, and a slow-timescale regulation mechanism, MTR establishes a general-purpose, reliability-aware learning architecture. This approach transcends the conventional optimization paradigm that relies solely on loss or reward signals, significantly mitigating error accumulation across diverse settings—including both reinforcement and supervised learning—and enhancing the system’s capacity to recover from erroneous beliefs, whereas standard algorithms frequently become trapped in incorrect convergence.

Technology Category

• Multiagent Systems: Multiagent Learning
• Machine Learning: Online Learning & Bandits
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Learning systems are typically optimized by minimizing loss or maximizing reward, assuming that improvements in these signals reflect progress toward the true objective. However, when feedback reliability is unobservable, this assumption can fail, and learning algorithms may converge stably to incorrect solutions. This failure arises because single-step feedback does not reveal whether an experience is informative or persistently biased. When information is aggregated over learning trajectories, however, systematic differences between reliable and unreliable regimes can emerge. We propose a Monitor-Trust-Regulator (MTR) framework that infers reliability from learning dynamics and modulates updates through a slow-timescale trust variable. Across reinforcement learning and supervised learning settings, standard algorithms exhibit stable optimization behavior while learning incorrect solutions under latent unreliability, whereas trust-modulated systems reduce bias accumulation and improve recovery. These results suggest that learning dynamics are not only optimization traces but also a source of information about feedback reliability.

Problem

• latent reliability
• wrong belief
• learning convergence
• feedback bias
• stable convergence

Innovation

• latent reliability
• learning dynamics
• trust modulation
• bias accumulation
• Monitor-Trust-Regulator