In the period six months to a year earlier, typical AI-agent reward hacking was myopic and could involve finding and editing tests the agent was not supposed to access, or otherwise manipulating the scoring process.

“the typical reward hack was a quite myopic. You know, you ask your agent to write a piece of software,”

“you have a bunch of tests in some different file that it's not supposed to have access to. And it.”

“And it like goes and finds the tests and edits them so they all pass or does something else to mess with the scoring process.”