The Hinge Loss Confidence Trap

Senior AI Engineer interview at Google DeepMind, and the interviewer asks:

“We’re training a massive binary text classifier. A junior engineer suggests using Hinge Loss because it creates a 𝘮𝘢𝘹 𝘮𝘢𝘳𝘨𝘪𝘯 and stops updating once a sample is correct, theoretically improving training stability. Why do we still prefer 𝐒𝐢𝐠𝐦𝐨𝐢𝐝 + 𝐋𝐨𝐠 𝐋𝐢𝐤𝐞𝐥𝐢𝐡𝐨𝐨𝐝 in production, specifically regarding the gradient signal on 𝘤𝘰𝘳𝘳𝘦𝘤𝘵 examples?”

Don’t say: “Hinge Loss is actually better because it’s robust to outliers. It stops penalizing the model once the prediction is correct (loss = 0), preventing overfitting to noise. Sigmoid is just an older method.”