grounding — Tying a model's answers to checkable sources — retrieved documents, live data, tool results — instead of letting it answer from memory alone.
Why it matters
A rigorous, well-illustrated walkthrough of why vanilla GANs are unstable and how Wasserstein distance fixes the gradient/convergence problems — useful groundingTying a model's answers to checkable sources — retrieved documents, live data, tool results — instead of letting it answer from memory alone.Full definition → for anyone building or debugging generative models.
Key quotes
“It is inspired by game theory: two models, a generator and a critic, are competing with each other while making each other stronger at the same time.”
“However, it is rather challenging to train a GAN model, as people are facing issues like training instability or failure to converge.”
