Prediction--Loss Alignment for Sampler--Robust Flow Matching Training
The episode discusses how generative models struggle with binary data because treating discrete bits as continuous values causes mathematical instability. Researchers propose aligning the prediction space with the loss space to eliminate gradient spikes. This method creates inherently stable training processes, which were tested successfully on images and MIMO communication signals.