3 papers
cs.LG2026
Learning Can Converge Stably to the Wrong Belief under Latent Reliability
Zhipeng Zhang, Zhenjie Yao, Kai Li +1
Learning systems are typically optimized by minimizing loss or maximizing reward, assuming that improvements in these signals reflect progress toward the true objective. However, w…
cs.LG2026
Learning to Trust Experience: A Monitor-Trust-Regulator Framework for Learning under Unobservable Feedback Reliability
Zhipeng Zhang, Zhenjie Yao, Kai Li +1
Learning under unobservable feedback reliability poses a distinct challenge beyond optimization robustness: a system must decide whether to learn from an experience, not only how t…
cs.LG2026
Training instability in deep learning follows low-dimensional dynamical principles
Zhipeng Zhang, Zhenjie Yao, Kai Li +1
Deep learning systems achieve remarkable empirical performance, yet the stability of the training process itself remains poorly understood. Training unfolds as a high-dimensional d…