Why do our AI models stop learning the second we deploy them?

Subscribe • Previous Issues Continual Learning Is Arriving in Pieces A while back I wrote about how startups are using reinforcement learning to make agents more reliable. A deeper problem behind that whole trend keeps resurfacing: a model can improve during training, but the moment it’s deployed, learning largely stops. A policy changes, a new edge case Continue reading "Why do our AI models stop learning the second we deploy them?" The post Why do our AI models stop learning the second we deploy them? appeared first on Gradient Flow .