[2603.21491] Learning Can Converge Stably to the Wrong Belief under Latent Reliability

Computer Science > Machine Learning arXiv:2603.21491 (cs) [Submitted on 23 Mar 2026] Title:Learning Can Converge Stably to the Wrong Belief under Latent Reliability Authors:Zhipeng Zhang, Zhenjie Yao, Kai Li, Lei Yang View a PDF of the paper titled Learning Can Converge Stably to the Wrong Belief under Latent Reliability, by Zhipeng Zhang and 2 other authors View PDF HTML (experimental) 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 ...