Research shows how to extract smaller, usable models from closed LLMs

A new paper on knowledge distillation demonstrates techniques for reverse-engineering the behavior of proprietary large language models into smaller, deployable alternatives. For indie makers relying on expensive API calls, this could mean cheaper inference and fewer vendor dependencies—if the approach scales beyond academic settings.