Egor Bogomolov

State-of-the-art code generation models struggle with evolving APIs, showing a stark performance drop that highlights a fundamental flaw in their training.

Code tokenizers are wasting tokens on source-specific noise, but a simple regularization technique can prune the fat and improve efficiency without sacrificing inference speed.