Unit-test rewards teach models to edit more: PreciseCoder cuts average edits from 7–15 lines to 3 without losing pass rate
Reinforcement learning from unit tests raises pass rates but also teaches code models to edit more and to invent bugs in programs that are already correct. PreciseCoder adds a signal for semantically verified edit precision and recall against a reference repair; on 4B, 9B and 27B models it cuts the average edit from 7–15 lines to 3 at a pass rate on par with unit-test-only training, and raises the no-edit rate on correct code to 47–52%.