Perplexity introduced Joint On-policy Learning and Teaching. It trains one model as both teacher and student. The teacher uses privileged information to guide the student. The student learns from dense feedback instead of sparse rewards. The teacher adapts its guidance to the student’s current ability. The method improves performance and training efficiency. It works on math, coding and agentic tasks. A paper describes the approach in detail.