dailyai.report

A new teacher-student framework called Distill-Belief decouples Bayesian correctness from inference efficiency. It uses a particle-filter teacher to provide dense information-gain signals, preventing students from exploiting approximation errors. This approach stops reward hacking during mobile agent training. Practitioners can now deploy faster belief models without sacrificing uncertainty estimation accuracy in physical fields.