dailyai.report

A new exploratory project uses black-box LLM autoraters to identify 10-20 key features within model transcripts. The process splits data into user turns, thoughts, and responses to uncover hidden behavioral correlations. This method helps researchers qualitatively analyze target models during RL training. It streamlines the discovery of novel behaviors in complex deployment distributions.