video1.0<iframe src="https://www.loom.com/embed/b46b2eba084b4fd58bb6fa1b32c713cd" frameborder="0" width="1662" height="1246" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>12461662Loomhttps://www.loom.com12461662https://cdn.loom.com/sessions/thumbnails/b46b2eba084b4fd58bb6fa1b32c713cd-b255c3a2c1c77bb1.gif130.608This Loom explains a continued learning plugin for QM that turns user interactions into training data in real time. It describes an API setup for collecting data during conversations, including a student teacher mode for SFT or policy distillation and a readout mode that uses A B testing for RL style training. The demo also covers a proactive teammate model that initiates help without being asked, gathering feedback via thumbs up or stop signals, and includes a smaller pipeline model to decide when to be proactive.